Inclusive Ocean Data for Decision-Making

INCLUSIVE OCEAN DATA FOR DECISION-MAKING

BY ARABA SEY AND CHRIS ROTHSCHILD

THE AUTHORS

This resource was prepared by Araba Sey and Chris Rothschild, under the auspices of the Ocean Nexus Center.

Araba Sey is a Principal Research Scientist with the Technology & Social Change Group at the University of Washington Information School. A citizen of Ghana, she has been formally educated in Africa, Europe, and North America. In this diversity of living and training contexts, she has experienced both privilege and disadvantage. She strives to maintain an awareness of how these experiences can affect her work and her interactions with others of greater and lesser privilege.

Chris Rothschild is a Senior Research Scientist with the Technology & Social Change Group at the University of Washington Information School. Born and raised in Hawai'i (although not native Hawaiian), his formative education was shaped by ‘āina ("land," including water systems) and uncles and aunties, helping him to understand the land and oceans through different lenses. Work and western education in the islands, continental US, and other countries made apparent the multiple and complex variations in how people around the world engage with their natural environments, and the privileges and limited experiences he holds. Chris' work seeks to build spaces in his actions and those of others for inclusivity of different approaches to understanding, using, and managing the 'āina.

ABOUT OCEAN NEXUS

Transformational change begins with understanding how our relationship with oceans deeply affects social justice and equity. In collaboration with The Nippon Foundation, Ocean Nexus champions transformational social change through actionable ocean governance research. Based at The Nippon Foundation Ocean Nexus Center, our team expands upon The Nippon Foundation’s program for policy research capacity building through an interdisciplinary social science research approach. At Ocean Nexus, we aspire to create equitable oceans for humanity.

ABOUT THE TECHNOLOGY & SOCIAL CHANGE GROUP

The Technology & Social Change Group (TASCHA) is a research center at the University of Washington Information School that explores the role of digital technologies in building more open, inclusive, and equitable societies.

ABOUT THE IMAGES IN THIS GUIDEBOOK

While we credit all the images used in this guidebook, we acknowledge and elaborate on two sources from which we drew heavily:

Adinkra symbols are incorporated into the headings of each chapter and scattered throughout the text. Adinkra are traditional visual symbols from Ghana used to represent concepts, values, and philosophies in the Akan language. In recent times, they have gained global recognition and renditions can be obtained from freely accessible online repositories. These renditions are not identical and may be accompanied by slightly different interpretations, but the core meanings are consistent. Our use of a particular interpretation or translation does not imply that that is the original or most accurate interpretation. We are grateful to the cultural communities from whom these symbols emerged and are maintained, as well as the communities that have toiled to make them freely accessible online.

To further our goal of incorporating different visual representations of social and research concepts, we are honored to be granted access to the artwork of Solomon Robert Nui Enos. Solomon is a Native Hawaiian artist, illustrator, and visionary. Born and raised in Makaha Valley (O'ahu, Hawai'i), Solomon hails from the well-known Enos 'ohana. Solomon is a selfdescribed 'Possiblist," whose art expresses an informed aspirational vision of the world at its best via contemporary and traditional art that leans towards Sci-Fi and Fantasy. His work touches on themes like collective-consciousness, ancestry and identity, and our relationship with our planet, all through the lens of his experience as a person indigenous to Hawai'i. More information about Mr. Enos can be found on his website: http://www.solomonenos.com.

CREDITS

Layout and design: Creative Communications, University of Washington

Cover art: Solomon Robert Nui Enos

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/.

Inclusive Ocean Data For Decision-Making is a guidebook produced by The Nippon Foundation Ocean Nexus Center in collaboration with The Technology and Social Change Group at the Information School at the University of Washington. It provides a practical tool for addressing environmental problems while ensuring that community values and needs are prioritized. The resource is grounded in the Ocean Nexus concept of ocean equity, that aims to dismantle systemic inequity through the governance of oceans.1

Typically, social equity approaches to ocean governance start with the premise of an environmental problem (such as overfishing or ocean acidification) that needs to be solved in an equitable manner. Ocean Nexus’ proposed approach flips this thinking to start with the premise of a social problem (such as racism or gender discrimination) that needs to be solved, and views ocean governance as one of the pathways to solve those problems. This calls for a reorientation of collaborative and multidisciplinary processes involving researchers, policymakers and communities — to define systemic issues and co-design ocean solutions and strategies that address the causes of inequities, rather than merely the symptoms of inequities.

This Inclusive Data for Decision-Making resource offers one method to pursue that reorientation. It prioritizes dynamics within decision-making processes, rather than the making of any single decision. Although similar in some respects, it differs foundationally from pre-existing community development approaches such as stakeholder engagement and participatory research. In the context of ocean governance, these approaches typically focus on the ocean problem, with the social aspects being incidental. This resource, on the other hand, is not an instrument for ocean interventions, although one of the end products could be a plan for some intervention. Instead, it is meant to guide a thinking process through which communities can identify and address the systemic issues that lead to inequitable distribution of both the impacts of environmental change and the benefits, risks, and costs of ocean governance. It also aims to support communities to identify their own socio-ecological issues, decide what related data they want, and make their own decisions about how to obtain and use that data for decision-making. In this way, the data theme serves a dual function: as a practical exercise on which to focus discussions of social and ocean equity; and a way to demonstrate how social inequities can be embedded into decision-making processes, even when decisions appear to be evidence-based.

AKOBEN |war horn symbol of a call to action, readiness to be called to action, and voluntarism

Source: http://www.epaghanaakoben.org/

… no data practice can fully escape the pull of non-innocent relations —the embeddedness in racial capitalism and colonialism — that shape data.

Acknowledging and learning to refuse this extractive logic through participatory knowledge making and contextualizing data, among other practices, is central … (Lourdes Vera and others) 2

1 Yoshitaka Ota and others. (Forthcoming). Defining ocean equity. In preparation with over 50 co-authors from Ocean Nexus. See also https://oceannexus.uw.edu/2022/07/06/2022-un-ocean-conference-presentation-on-assessing-global-ocean-equity/

2 Lourdes Vera and others. (2019, p.1014). When data justice and environmental justice meet: Formulating a response to extractive logic through environmental data justice, Information, Communication & Society, 22(7), pp.1012-1028, DOI: 10.1080/1369118X.2019.1596293

ACKNOWLEDGEMENTS

We have drawn on the works of countless individuals, institutions, and communities whose observations of the seen and unseen world have enlightened us on the importance of embracing different ways of knowing. We acknowledge and thank all who supported us directly and indirectly by inspiring ideas; assisting with connections to knowledge holders; reviewing and commenting on drafts; providing financial and moral support, encouragement, and validation; and freely sharing their intellectual works. We apologize to any group or individual we have inadvertently failed to mention.

University of Washington EarthLab, the Ocean Nexus community, University of Washington iSchool, Technology & Social Change Group, Malia Akutagawa, Stacey Aldrich, Francisco Blaha, Brooke Campbell, Kevin Chang, Kat Chung, Edwin Boachie-Yiadom, Theophilus Boachie-Yiadom, Aurélie Delisle, Solomon Robert Nui Enos, Denise Espania, Sonja Evensen, Keita Furukawa, Sarah Inman, Kwesi Johnson, Ryan Kelly, Jack Kittinger, Terrie Klinger, Alex Mawyer, Manuel Mejia, Ifeasichina Okafor-Yarwood, Isaac Okyere, Yoshikata Ota, Philip Prah, Noelani Puniwai, Ryan Rykaczewski, Kirk Sato, Dirk Steenbergen, Miwa Tamanaha, Izumi Tsurita.

INTRODUCTION 1

It has long been a human dream that people (demos) rule (kratos) their destiny. The notion of inclusion carries with it expectations of being heard, of obtaining a favourable outcome, of involving everyone who has a stake and of sharing power with those who must rule. (Tim

OBJECTIVES OF THIS GUIDE

Decision-making is a part of our everyday lives. Whether consciously or subconsciously, and regardless of how our communities go about it, we all use data (pieces of information) to make our decisions. Data in its many forms helps us to define and understand an issue, build a plan for addressing it, and assess how the plan is working. However, the processes and contexts of data production can affect the quality and appropriateness of the decisions made. This guidebook focuses on how we obtain and use the data that informs decisions — where it comes from, how we go about finding and using it, and, importantly, how to broaden the net of who participates in the data

FAWOHODIE | independence symbol of independence, freedom, emancipation

From the expression: “Fawohodie ene obre na enam” — Independence comes with its responsibilities.

Source: http://www.adinkra.org/htmls/adinkra/fawo.htm

production process. The objective is to outline a process that you and your communities can use to plan towards using data for decision-making in ways that reflect the realities of your communities and are inclusive of different groups, practices, and values.

The guidebook is designed for community organizations, such as nonprofits and public libraries, to plan decisionmaking activities with their communities. Being on the frontlines, community organizations are uniquely placed to provide insights on local context, practices, customs, and priorities for meaningful decision-making processes and appropriate approaches to community engagement.

Using data effectively for decision-making requires not only asking the right questions, but also questioning the answers that we accept as valid — for example, who did we receive the answers from, how do we react to different sources of information, and do we prefer some sources and answers over others? The framework and activities here offer opportunities for you to:

O’Riordan)1

1 Tim O’Riordan. (2005, p.175). Inclusive and community participation in the coastal zone: Opportunities and dangers. In J. Vermaat, W. Salomons, L. Bouwer, & K. Turner (Eds.), Managing European Coasts (pp. 173–184). Springer. https://doi.org/10.1007/3-540-27150-3_9

• Explore questions and answers on issues of concern within your organization and community

• Develop inclusive strategies to use data to guide the decisions you make about these issues

• Demonstrate the ability of communities to participate fully in data and knowledge generation processes

Our hope is that, in the long term, practicing these activities will demonstrate the benefits of genuinely participatory decision-making, and ultimately lead to more inclusive knowledge production processes that draw on the diverse realities of communities, researchers, and policy makers.

WHAT IS INCLUSIVE D4D?

Data for Decision-Making (D4D) is the process of planning, capturing, assessing, and using data to make decisions. Inclusive D4D is D4D that:

• Finds ways to enhance community participation in the full range of activities that go into decision-making

• Acknowledges and incorporates local knowledge and context

• Allows room for different knowledge systems

“Data” refers broadly to both quantitative (numbers) and qualitative (words, ideas) information, and can be obtained in a variety of ways. Approaches to data are also framed by value systems that vary across contexts (for example, some cultures prefer numbers to stories). Users of this guide should incorporate or replace our definitions with local definitions and approaches to data. Before settling

on a decision-making approach, we highly recommend reading Section 4 for guidelines on fostering inclusive decision-making.

WHY INCLUSIVE D4D IN OCEAN MANAGEMENT?

The ongoing data revolution has created opportunities for governments and organizations to produce and use data more effectively for environmental policy and decision-making. At the same time, this trend has opened avenues for communities and individuals to engage more meaningfully with decision-making through civic action and dialogue related to environmental protection. Despite this, structural and other barriers prevent many groups (e.g., those with lower incomes, indigenous communities) from fully participating in the processes that produce the knowledge that feeds environmental policy and action within their communities and beyond. These populations remain pervasively underrepresented in decision-making activities or are only included in data collection stages, and yet they tend to be the ones most significantly affected by the negative impacts of environmental changes.

Decision-making is an embodied practice, and the data and knowledge that go into it are not just words and numbers. It is inextricably intertwined with people, the land, the ocean, animals, culture, ancestors, and many other crucial aspects of ourselves that we carry in our personal and collective journeys. As such, leaving any groups out of the decision-making process deprives us all of a piece of our world — the knowledge, experience, and relationships of the excluded — and makes our decisions and actions incomplete. Inclusive D4D seeks to expand opportunities for the voices of affected communities to be brought into the entire D4D process.

Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/

COMPONENTS OF A DECISION-MAKING PLAN

Across cultures, decision-making approaches may be similar or very different. Regardless of culture, however, decision-making tends to include some or all of the following activities:

• Identifying the issue that needs a decision

• Deciding what information (data) is needed to make the decision

• Choosing the data collection methods

• Gathering the information

• Assessing the information

• Making a decision

• Implementing the decision

• Evaluating the effectiveness of the decision

This resource provides guidance on how to undertake each of these activities in an inclusive manner. Developing a decision-making plan is like planning a journey or a walk. You may make a targeted plan to go to a specific location. Or you may make a general plan to travel in a broad direction without a specific endpoint in mind. This resource can be used in both scenarios. Depending on your needs, you can start with any section, use all sections, or focus only on some sections of the resource to create your inclusive D4D plan. You can also use the resource to evaluate the inclusiveness of plans you have already made. Having a plan for inclusive D4D can enable you to be more intentional about incorporating diverse voices.

knowledge and knowledge creation is inspired by the work of thinkers such as Fikret Berkes, Patrick Nunn, Linda Tuhiwai Smith, Shaun Wilson, and Nālani Wilson-Hokowhitu.

• The boundaries that define our communities and the impacts of our decisions vary across cultural and political geographies, and natural and spiritual environments. These boundaries need to be considered in decision-making processes.

ESE NE TEKREMA | the teeth and the tongue symbol of friendship and interdependence

The teeth and the tongue play interdependent roles in the mouth. They may come into conflict, but they need to work together.

Source: http://www.adinkra.org/htmls/adinkra/esen.htm

Community-building is intertwined with livelihoods and environmental resource management in Native Hawaiian societies. Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/

Keep in mind:

One, everything is related — if you choose to explore only some sections of the guide, build in time to consider how those sections connect with other components of decision-making.

Two, whichever decision-making step you are addressing, strive to be as inclusive as possible.

KEY PRINCIPLES

Everyone can benefit from being deliberately reflective about issues of inclusion and exclusion. This can foster intentional action to offer pathways for different kinds of knowledge and ways of knowing to speak and be heard. Though by no means universal, the key principles listed here can help open up these pathways. This framing of

• Relationships and relationality are everywhere. All things are related, whether past, present, or future. Although you will be focusing your attention on specific issues, keep in mind the variety of potentially relevant connections to other issues and contexts.

• Knowledge creation comes with accountability. We are accountable to the knowledge we acquire and use, and the relationships embedded in that knowledge. Accountability means respecting the knowledge shared with us, the people who shared it, and the human and non-human communities that our decisions might impact. This has multiple dimensions, including: abiding by the expectations of those who allow you into their communities and lives; representing information accurately and respectfully; giving participants the opportunity to review research; acknowledging and correcting errors quickly and humbly; supporting your research interpretations with the actual voices of participants; and actively seeking ways to ensure that the research process is mutually beneficial.

• Knowledge and practice are not neutral. Data about us, the data we collect, or data we use to make decisions are all a reflection of our perspectives and manifest in our cultures and practices. Acknowledging this gives us the

mindset to self-reflect and hold ourselves accountable to the knowledge and communities we work with.

• All methodological approaches have validity. We do not prioritize specific methods, nor do we expect methods to align with each other in process or results. Participants should be confident in using the methods and expertise that have been historically developed within local communities. These may blend Western and non-Western approaches or employ one or the other. It is not necessary to compare or justify one through the lens of another.

Like any journey, successful inclusive decision-making requires ensuring everyone on the team understands the destination and how they will work together to get there. Decision-making often fails when we take this common vision for granted, especially when we do so on behalf of others who are not present. Your organization might be new to community engagement or might already have a long history of implementing community participation processes. Our goal is not to introduce a new process but to offer a framework for continually assessing: (1) how inclusive your processes are in practice across the spectrum of decision-making activities and (2) how representative the outcomes are of the interests and desires of affected communities.

Reflexivity entails, among other things, the acknowledgment of who we are, our experiences, our individuality as researchers, the communities we belong to, and how our values and social position influence how we perceive issues affecting us and our communities. Being reflexive can make us more aware of the limits of our knowledge, how our own behavior plays into organizational practices, and why such practices might marginalize groups or exclude individuals.2 As we experience and assess new ideas, reflecting on our own perceptions and hidden assumptions compels us to confront the notion that knowledge is objective and that every situation has one “true” explanation.

Reflexivity can help us value the experiences of others, acknowledge that different realities can exist in a single community, and become sensitive to the myriad ways in which different community members experience reality. We may not all experience issues in the same way but being reflexive can foster humility and accountability. Reflexivity can be personal (identifying your own assumptions, motives and framings), interpersonal (assessing your positionality — the nature of your relationships with others), or collective (assessing the entire engagement processes and its impact on outcomes). 3

DIG A LITTLE DEEPER: REFLEXIVITY AND POSITIONALITY

2 Gillie Bolton. (2010, p.14). Reflective practice: Writing and professional development (3rd Ed.). Sage. 3 Ruth Nicholls. (2009). Research and Indigenous participation: Critical reflexive methods. International Journal of Social Research Methodology, 12(2), 117–126. https://doi.org/10.1080/13645570902727698

HOW TO USE THIS GUIDE

The sections in this guide will take you through the various steps of creating a D4D plan that is inclusive not only in the creation process but also in its content. You can start with a specific decision issue in mind or work more generally to prepare for future decision-making. The goal is to create a decision-making plan that reflects your vision, your context, your practices and your realities.

DENKYEM |crocodile symbol of adaptability, cleverness

From the proverb,“ denkyem da nsuo mu nanso home mframa” — “The crocodile lives in water yet it breathes air.”

Source: https://www.adinkrasymbols.org/symbols/denkyem/

Each section contains the following:

1. Description of the D4D area

An overview of the key features of that aspect of decision-making and some suggestions or good practices.

2. Case studies

A case study to provide real-life examples of the D4D area.

3. Activities and worksheets

Opportunities to build out sections of a D4D plan. They cover assessments of how decision-making is done in your community and in the larger political context, and where these processes do and do not align. The goal is to surface any unique and important aspects of your processes and incorporate them into the plan on their own merit. At the same time, you will think through areas where your plan does not align with other local structures. You will discuss whose voices are normally involved or left out of the process what the implications might be, and how to make your processes more inclusive. As you move through the guide, you will revisit and refine the plans you have crafted in previous activities. The purpose of each activity is indicated as well as sample worksheets for capturing your decisions, but you are free to decide how to conduct the activity to meet the desired goals.

Adaptability

Contextualization and adaptability are important to allow communities to determine which areas of emphasis in the D4D process are most relevant to them. We received and are grateful for the input of our many partners that helped inform the structure and definitions used here. However,

we also encourage people using this document to include their own locally relevant definitions, examples, and case studies where appropriate. Adaptability is also important since the decision-making process needs to be responsive to new ideas, practices, realities, and the changing ways information and data are captured and used for decisionmaking. This resource, and your plan, should be dynamic and responsive to your evolving environments.

ACTIVITY: DEVELOP CODE OF CONDUCT

Making a safe space: Before you begin, consider how you can create a safe space to work together on this plan. Some of the issues you discuss may feel personal and generate difficult conversations, so it is important to have a discussion leader who can steer those difficult conversations. A safe space means an atmosphere of openness to all ideas has been established; all participants are thoughtful in how they bring up and discuss issues; and important community rules of engagement (such as social protocols) are appropriately followed to make people comfortable voicing their opinions to others.

In this activity, you will develop ethical guidelines for the process of creating your D4D plan. This code will shape the way you engage with each other to create your plan. This will be something like an internal social contract. Respecting the different ethics and norms of a community is important for creating a space where individuals feel valued and not just willing but inspired to share. For example, in some areas a prayer or other type of evocation might be important to set the stage for discussions; in some communities it might be essential for opinions to be kept anonymous in reports; while in others, speakers might want to be identified by name.

Define your code of conduct in Worksheet 1

Sample resource on creating a code of conduct

EBAN | fence symbol of love, safety, and security

Source: http://www.adinkra.org/htmls/adinkra/eban.htm

Worksheet 1: Define your code of conduct

Purpose: Decide what code of conduct will guide your discussions about the D4D plan

List and briefly describe all the behaviors that you consider important to create a safe and brave discussion space. For example:

1. Try not to take questions or disagreements personally.

2. Keep your mobile phone on silent and leave the room if you need to use it. Select which ones to include in your code of conduct. For manageability, try to pick five to ten items for the final code.

Reflect on the following questions:

• Are these rules sufficient to create a safe discussion space for all participants?

• Whose ideas and preferences were included in making these rules?

• How were decisions made about what to include in the code of conduct?

DEFINING COMMUNITY 2

setting

Umia ka hanu. Literal: Hold the breath. Meaning: Be patient. Don’t give up too easily. (Pukui & Varez)2

Although this guide may be used in a non-linear manner, there are some preliminary steps that we encourage users to complete before starting to draft a decision-making plan. This process can be slow and requires patience — taking the time to reflect and allow personal experiences, histories, and ancestral knowledge to interact with recent insights to “surface as new knowledge” (Kū Kahakalau, 2019). 3 As you go through the first few sections of the guide, be patient and resist the urge to skip or rush through these critical steps.

PURPOSE OF THIS SECTION

Before planning a journey, it is useful to ask ourselves the following questions — why do we want to take this trip? Who will go with us? How will we plan and manage the trip? What will we bring back or leave behind? Who might be affected (positively or negatively) by our journey? This examination takes time and commitment to addressing any difficult issues that may arise from people’s experiences, memories, or histories. By the end of this section, you should ideally be in a “raised state of consciousness” so that when the detailed planning begins, you will have a heightened sensitivity to existing power structures and the

NYANSAPO | wisdom knot symbol of wisdom, ingenuity, intelligence, and patience

Source: http://www.adinkra.org/htmls/adinkra/nyan.htm

1 Shawn Wilson. (2008, p.69). Research Is Ceremony: Indigenous Research Methods. Halifax: Fernwood Publishing.

2 Mary Kawena Pukui & Dietrich Varez. (1983, p.314). 'Olelo No'eau: Hawaiian Proverbs & Poetical Sayings. Bishop Museum Press.

3 Kū Kahakalau, (2019, p.14). MĀ’AWE PONO: Treading on the Trail of Honor and Responsibility. In Nālani Wilson-Hokowhitu (Ed.). The Past Before Us: Mo’okū’auhau as Methodology (pp.9-27). University of Hawai’i Press.

... research is a ceremony. In our cultures, an integral part of any ceremony is

the stage properly. When ceremonies take place, everyone who is participating needs to be ready to step beyond the everyday and to accept a raised state of consciousness. (Shawn Wilson)1

CASE STUDY: DEFINING COMMUNITY BASED ON PLACE ATTACHMENT

In a study of attachment to the Great Barrier Reef (GBR), the researchers identified four communities of place attachment — Armchair Enthusiast, Reef Connected, Reef User and Reef Disconnected. These communities traversed national residents, indigenous residents, commercial fishers, tourists and tourism operators. Not surprisingly, the Reef Connected group (which had the highest level of place attachment) contained the most people with physical ties and direct experience of the GBR (commercial fishers and tourism operators). However, they also found that “ongoing direct experiential processes do not always lead to the formation of emotional bonds and that such bonds can form without ongoing firsthand experience” (p10081).

For example, Armchair Enthusiasts, who had the second highest level of place attachment, included about 30% of international stakeholders and more than 47% of national stakeholders residing far from the GBR.

1. What community or communities do you consider yourself to belong to?

2. How similar or different are you from other people in that community?

3. Which community members should have the greatest say in how the community addresses environmental issues?

variety of groups for whom your decision-making journey has implications. You will already be thinking about the values and principles that would enable you to engage meaningfully with them for an inclusive experience.

Results:

1. A description of your community

2. Assessment of community members’ power, influence and interest in environmental issues

WHO IS THE “COMMUNITY”?

An inclusive Data for Decision-Making plan incorporates the collective needs, knowledge, experience, networks, and relationships of the individuals and groups it affects. In accordance with the principle of relationality, our communities can extend beyond people, to include the natural and spiritual environments as well. For example, a community can include animals, plants, and ancestors.

The definition of “community” has evolved significantly over the years. Traditionally, the term has been used to signify close-knit in-person and geographically bounded social groups. For example, Greenfield describes a community as “a small-scale social entity with social relations based on close personal and lifelong ties — e.g., a rural village.”4 However, increasing mobility (physical and digitally-driven) has enabled different types of communities to emerge based on features other than location (such as profession, personal interests or demographic characteristics). Such communities could exist in physical spaces or be entirely virtual or online. Communities of practice, defined as “groups of people informally bound together by shared expertise and passion for a joint enterprise”5, can emerge over vast distances. This means that the community of relevance to a particular environmental issue could include people who do not reside in the geographic vicinity. Similarly, people with residential or kinship ties to a location might not necessarily feel an identical sense of community. This has implications not only for who can, but also for who wants to participate in addressing an issue.

NKONSONKONSON | chain symbol of unity and community

Source: https://www.kasahorow.org/node/80

An important aspect of decision-making is to be aware who your perceived and actual community is, and whose voices you are including or not including. If we are honest and transparent about who is and is not represented in our decision-making processes, we can actively seek ways

4 Patricia M. Greenfield. (2020, p.325). Multilevel theory of emerging technologies: Implications of historical transformation for human development. Human Behavior and Emerging Technologies, 2(4), 325–335. https://doi.org/10.1002/hbe2.222.

5 Etienne C. Wenger & William M. Snyder. (2000. p.139). Communities of practice: The organizational frontier. Harvard Business Review, 78 (1), 139–146.

Source: Georgina Gurney et al. (2017). Redefining community based on place attachment in a connected world. Proceedings of the National Academy of Sciences, 114(38), 10077–10082. https://doi.org/10.1073/pnas.1712125114

to include them or, at the very least, acknowledge the existence of the people whose voices do not have full representation.

The idea of a community tends to bring up visions of a unified population working towards common goals. In reality, despite being bound by some common characteristics, communities consist of diverse groups with different experiences, opinions, needs, goals, and degrees of power. Oftentimes, the profile of one group (or a small number of groups) is elevated over that of others due to that group’s greater size or access to important resources such as finances, political power or knowledge. This group effectively becomes the dominant group in the community, and members of that group tend to consciously or unconsciously impose their will on the entire community. To mitigate the influence of more powerful actors, reflect on the extent to which your definition of community includes people with diverse characteristics, especially people with different types and amounts of power. In your planning activities, set up structures to ensure that the dominant or more powerful groups do not monopolize the agenda.

What features characterize dominant groups in your community?

Political power

Wealth

Gender

Education/knowledge

Profession

Citizenship

Ethnicity

Sexual orientation

Physical ability

Age

Other feature _____________________________________________

How would you classify yourself – do you fall into any dominant group category?

MAKO | peppers symbol of inequality and uneven development

From the Akan proverb “Mako nyinaa mpatu mmere”

— “All peppers do not ripen simultaneously.”

Source: https://www.kasahorow.org/node/80kasahorow.org/ node/80

METHODS FOR DEFINING YOUR COMMUNITY

Two useful methods you can use to define your community are stakeholder analysis and network analysis. Both options provide a way to visualize a community, understand relationships, and develop strategies for interactions among community members.

Stakeholder analysis is the process of identifying the people and organizations that have a vested interest in the issue you are working on, and assessing their characteristics (such as their knowledge, interests, standpoints, and social/political influence). Stakeholders could be traditional leaders, national politicians, specific government departments, people living in a specific locality, people with particular occupations, or consumers of a product or service, for example. Conducting a stakeholder analysis enables you to determine whose circumstances need to be accounted for in addressing an issue, their degree of interest in the issue, who holds the most and least power, and whose participation or support is critical for an inclusive process and successful implementation of decisions. A detailed stakeholder analysis involves many stages, but a simplified process can be performed with three key steps:

1. Brainstorm — list all the actors who could have an interest in the issue, especially those that might be affected and those that can affect implementation of any decisions made.

2. Describe — describe relevant characteristics of the identified stakeholders. This might require seeking additional information.

3. Assess — analyze the distribution of the characteristics across stakeholders. The outcome of the assessment is often presented visually. Figure 2.1 shows two sample templates based on power, interest, and importance characteristics, including recommendations on how to engage with different groups.

Network analysis involves mapping out the connections between different actors within a system. It facilitates understanding about what types of relationships exist (e.g., power, influence, and motivations of actors) and how they affect behaviors and outcomes (Figure 2.2). Social network analysis could be an important tool in community development work6 as it can be used to understand a community before beginning an intervention or to understand project outcomes after an intervention.

This

GOOD RELATION

A close and good working relationship must be established with this group

LOW

MONITOR

This group may be the source of risks, and will require careful monitoring and management

Sources: https://www.mindtools.com/pages/article/newPPM_07.htm; https://www.thegrassrootscollective.org/stakeholder-analysis-nonprofit

Development Journal, 48 (1), 40–57. https://doi.org/10.1093/cdj/bss013

Figure 2.1: Sample Templates for Stakeholder Prioritization

6 Gretchen Ennis & Deborah West. (2013). Using social network analysis in community development practice and research: A case study. Community

The following steps are a simplified representation of social network analysis:

1. List relevant actors (individuals, groups, organizations, etc.).

2. Indicate what connections exist among them and the nature of those connections.

3. Assess the characteristics of the network (e.g., how many links there are?, which actors seem to hold prominent positions within the network?, to the extent to which links congregate around a few central actors?, the existence of disconnected actors within the system.)

Source: https://www.rescue.org/sites/default/files/document/1263/socialnetworkanalysise-handbook.pdf p.5

Figure 2.2: Sample Visualization of a Social Network

Things to consider when defining your community

• On what basis are you defining the community? For example:

 location

 shared culture

 shared history

 shared values and beliefs

 shared experiences

 shared knowledge base

 membership

 relationship

 influence

 sustained interaction

 commitment

 connection

 shared emotional connection

 social solidarity

 reciprocity

 reinforcement

 trust

• Do you want to define the community narrowly or broadly?

• In your society’s worldview, are non-human elements considered part of the community?

• Do the implied members of your community feel like/consider themselves members of the community?

• Are there other geographically near or far groups that you might consider part of your community?

• Which community members have more power and which have less or none?

Resources on stakeholder analysis:

Using a stakeholder analysis to identify key local actors:

Resources on network analysis: Net-Map toolbox:

Stakeholder analysis guidelines:

Social network analysis handbook:

ACTIVITY 2.1: DEFINING COMMUNITY

Define your community in Worksheet 2

ACTIVITY 2.2: REVISIT PREVIOUS DECISIONS

Revisit the code of conduct you developed in Activity 1. Have you captured ethics and norms from all the voices you have now defined as part of your community? Discuss and update the code as necessary.

Revisit your code of conduct in Worksheet 1.

Worksheet 2: Define your community

Purpose: Define what constitutes your community in particular decision-making contexts

Decide what method you will use to define your community. This could be a stakeholder analysis, network analysis, or other method more suitable for your context.

1. Using the chosen method, list and describe groups (human and non-human) that could affect or be affected by the decision-making issue to be discussed.

2. Does the group have representatives in the room?

3. If it does not, discuss what to do about it.

4. If necessary, repeat this process for different decision types.

Sample Worksheet DESCRIPTION

Worksheet 1 update: Re-define your code of conduct

Purpose: If necessary, update the codes of conduct to reflect the values of any new groups

1. List the behaviors selected in Worksheet 1

2. Add any new behaviors emerging from the identification of new community groups

3. Assess whether the identified behaviors are:

i) Unique or important to your community only

ii) Unique or important to a dominant group in your community only

iii) Unique or important to an external community only

iv) Shared with other communities

4. Decide which behaviors to include in the final code of conduct

Reflect again on the following questions:

• Are these rules sufficient to create a safe discussion space for all participants?

• Whose ideas and preferences were included in making these rules?

• How were decisions made about what to include in the code of conduct?

3

IDENTIFYING DECISION-MAKING CULTURES

PURPOSE OF THIS SECTION

This section reviews different approaches to decisionmaking and their potential impact on the inclusiveness of decision-making.

Results:

1. A description of the main decision-making culture or cultures within your community

2. An assessment of how inclusive those cultures are of different affected groups

3. A description of the decision-making culture you want to adopt and the strategies you will use to build inclusiveness into the planning process and the plan itself

AKOFENA | sword of war symbol of courage and authority

Source: http://www.adinkra.org/htmls/adinkra/akofena.htm

1 Etienne Wenger. (2010, p.8). Communities of practice and social learning systems: The career of a concept. In C. Blackmore (Ed.), Social learning systems and communities of practice (pp. 179–198). Springer Verlag and the Open University. https://doi.org/10.1007/978-1-84996-133-2_11.

2 Hilary Berger. (2007, p.387). Agile development in a bureaucratic arena — A case study experience. International Journal of Information Management, 27(6), 386–396. https://doi.org/10.1016/j.ijinfomgt.2007.08.009

The definition of the regime of accountability and of who gets to qualify as competent are questions of power. (Ettiene Wenger)1

… the ‘actual’ working culture of an organizational may differ from the organization’s own perceived culture (Hilary Berger)2

DECISION-MAKING CULTURE AND THE DECISIONMAKING PROCESS

As noted in Section 1, using data for decision-making is essentially a research process that includes:

• Identifying the issue that needs decisions

• Deciding what data (information) is needed to make the decision

• Deciding how the data will be obtained

• Gathering the data

• Assessing the data

• Making a decision based on the data

• Implementing the decision

• Evaluating the effectiveness of the decision

These activities may take different shapes, occur in different sequences, include different or additional steps (e.g., Box 3.1), and differ in degree of inclusiveness depending on the decision-making culture of the community and the decision-making style of individual key or dominant decision-makers.

Box 3.1: Eight phases in an indigenous research and decision-making process3

1. ‘Imi Na’auao — Search for Wisdom

2. Ho’oliuliu — Preparation of Project

3. Hailona — Pilot Testing through Action Research Project

4. Ho’olu’u — Immersion

5. Ho’omōhala — Incubation

6. Ha’iloa’a — Articulation of Solution(s)

7. Hō’ike — Demonstration of Knowledge

8. Kūkulu Kumuhana — Pooling of Strengths

Decision-making cultures

A decision-making culture can be defined as the pattern of shared values and behaviors that shapes the way decisions are made within a community. It includes preferences for how decision-making responsibilities are distributed, beliefs about who qualifies to make decisions, the processes by which decisions are made, the role of information (data) in those processes, and the criteria for making choices. For example, in a bureaucratic system, the decision-making culture tends to be characterized by concentration of decision-making power at topmanagement levels; strict boundaries regarding who makes which decisions; extremely formal processes; and high value placed on technical expertise, predictability and efficiency.4 Bureaucratic structures do not lend themselves naturally to community participation, since this would require decentralized decision-making, participation of non-experts, and high tolerance for unpredictable outcomes. While bureaucracies might use efficiency as a decision criterion, other types of systems might prioritize profitability, living or ancestral relationships, social responsibility, or equity, for example.

Decision-making cultures can be defined along a variety of dimensions such as organizational type (e.g., private or private sector), approach to information (e.g., data-driven or experience-driven), degree of or approach to collaboration (e.g., authoritarian or democratic, majority rule or consensus-building), priorities (e.g., cost-effectiveness or equity), and processes (e.g., rules-based or relationship-based).5 6

3 Kū Kahakalau. (2019, p. 18). Mā’awe Pono Treading on the trail of honor and responsibility. In N. Wilson-Hokowhitu (Ed.), The Past Before Us — Mo’okū’auhau as Methodology. University of Hawaii Press.

4 Robert W. Kweit & Mary G. Kweit. (1980). Bureaucratic Decision-Making: Impediments to Citizen Participation. Polity, 12(4), 647–666. https://doi.org/10.2307/3234304

5 KnowledgeWorkx. (2013, January 6). Decision-Making (12 Dimensions of Culture #8). KnowledgeWorkx Insights. https://insight.knowledgeworkx.com/articles/global-intelligence/353/12-dimensions-of-culture-decision-making.

6 Paul C. Nutt. (2006). Comparing Public and Private Sector Decision-Making Practices. Journal of Public Administration Research and Theory, 16(2), 289–318. https://doi.org/10.1093/jopart/mui041

Who qualifies to participate in marine-related decision-making in your community?

CASE STUDY:

OYSTER FARMING DECISION-MAKING CULTURE IN HINASE, JAPAN

Oyster farming in Japan is largely done by small-scale family businesses. This is the case in Hinase, an area west of Osaka located on the Seto Inland Sea. Hinase is a fishing community supplying resources such as oysters, shrimp, blue crab, sea bream, and sole. Oyster farmers are located on the coast of the mainland as well as on two small outlying islands. Although fishers there began small-scale testing of oyster farming in the early 1900s, it wasn’t until the 1960s that it became a significant aquaculture activity.

To enable participation and collective power, oyster farmers are represented through membership in fishing co-operatives (co-ops). These co-ops provide a key foundation for the decision-making culture of oyster farming. In fact, the majority of people who participate in fishing activities belong to a local Fisheries Cooperative Association (FCA). Although some oyster farmers are active participants in the co-ops, the majority participate through indirect representation, as the co-op works directly with government officials, scientists, and other organizations to make decisions about activities such as farming processes and limits.

The voice of oyster farmers is just one of several groups within the fishing community, as set net, gill net, and trawling fishers are also part of the co-ops. Chief board members represent each fishing method and each island within the co-op. Power is not shared equally among these groups. Oyster farmers in particular have not built up the same historical power as other groups, partly because oyster farming only started receiving significant attention in the 1960s. Catch size, income, and the number of fishers also interplay with historical power to impact the relative power of each group. This causes shifts within the co-op. Therefore, the power of each group can change. As more people move into oyster farming and generate more income, oyster farmers are accruing more power.

FCA membership in Hinase is based on the family rather than the individual. Fishing rights, therefore, are inherited and cannot be expanded through heirs. As such, there is also a family-level decision-making culture within Japanese oyster farming. Furthermore, just as with organizations, individuals with more occupational history have more power. This manifests itself in elders having more power than younger farmers. Males also have more power than females, who tend to do less labor-intensive activities and generally do not see themselves as decision-making leaders.

Summary based on conversations with key informants

1. What decision-making culture or cultures can you identify in Japanese oyster farming communities?

2. How similar or different are they from decision-making in your community? What are some strengths and weaknesses of these cultures?

Hawaiian ancestors managed their offshore fisheries by building artificial reefs or 'fish houses'. This tradition connects with the building of fishing ahu (altars) to honor the Akua (gods) and the ancestors who protected and passed down this sacred form of aquaculture. Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/

Individual decision-making styles

The way people make decisions draws from the community’s decision-making culture but also has some features that are more individualized and based on personal preferences. Boogaard identifies four decision-making styles, while Marteney identifies similar types but adds layers related to emotion and assertiveness (Boxes 3.2 and 3.3).

Box 3.2: Decision-making Styles

Conceptual Visionary individual. Likes to develop ambitious, innovative solutions. Makes decisions based on big-picture, future-oriented thinking and brainstorming.

Directive Independent individual. Makes decisions independently. Depends on their own knowledge to assess options and make decisions.

Behavioral Relational individual. Prioritizes people’s feelings. Seeks reactions/consensus from others before making decisions.

Analytical Data-driven individual. Relies on data to make decisions. Prefers to gather as much information as possible before making decisions.

Source: Kat Boogaard (2020). Decision-Making Styles for Great Leaders: Which One Are You? https://blog.hubstaff.com/decision-making-styles/

Box 3.3: Decision-making Styles

Observing

rather

Concscious

patient, we need more data"

makes waste. Strike while the iron is hot""

Musk Unoncscious

Makes Decisions On emotions rather than facts

the decision?"

Makes Decisions

Robin Williams

Decision-making cultures and styles are not inherently good or bad — each one has strengths and limitations and can be effective or ineffective in different circumstances. However, some scholars argue that certain styles are more effective than others (Figure 3.1).

Decision-Making Effectiveness

Command leader Authoritative

Participation leader Directive

Flexibility and Freedom

DECISION-MAKING, POWER AND CHANGE

Irrespective of what form it takes, the decision-making culture of a community is embedded with power dynamics. Certain types of people are more likely to be influential in the entire decision-making process, some may only be allowed entry or say in specific stages of the process, and some might have no participation or influence at all. In academic or policy research, scientists often control all stages of the research, with limited opportunities for communities to participate except as research subjects, local informants, or language interpreters. Rarely do researchers include communities in the entire research

and decision-making process, which could include such basic but important activities as defining the issues to be studied or the best way to capture data in their community. Where deeper participation is enabled, this tends to be in the form of research validation exercises at the end of the research, although in recent times, and especially with environmental research projects, scientists have started including communities more directly in data collection activities as well. Ultimately, communities and/or certain segments of a community often must accept decisions that they or their representatives did not participate in making.

Figure 3.1: Effectiveness of Decision-making Styles

Adapted from Adam Sulich, Letycja Sołoducho-Pelc, & Marcos Ferasso. (2021, p.10). Management Styles and Decision-Making: Pro-Ecological Strategy Approach. Sustainability, 13(4), 1604. https://doi.org/10.3390/su13041604

What type of decision-maker are you? What are possible strengths and limitations of different types?

Analytic

Laissez faire leader

Behavioural Laissez faire leader

Servant leader

In these varying scenarios, community members might feel disenfranchised and disengage from important community issues. Nevertheless, they will make their own decisions about whether to comply, resist, or circumvent directives that are handed down by decision-makers. Conversely, people in powerful positions can make conscious efforts to be inclusive, no matter the decision-making culture. However, for these efforts to be successful, they must be accompanied by a preparedness to reflect on one’s own power status, challenge the status quo, offer spaces for meaningful participation, and potentially make significant changes to the existing decision-making culture. This type of change cannot be imposed, nor does it happen overnight — therefore achieving greater inclusiveness also requires being willing to allow time for the decisionmaking culture to evolve over time. The purpose of this

D4D resource is to lay foundations for this type of change — setting the stage to enable participation of as many different community groups in as many decision-making activities as possible.

ACTIVITY 3.1: DECISION-MAKING CULTURES

• Outline your decision-making culture and inclusion strategies in Worksheets 3a and 3b

ACTIVITY 3.2: REVISIT PREVIOUS DECISIONS

• Revisit your code of conduct and definition of community. Do they reflect the decision-making culture you have agreed on? Discuss and update as necessary.

Maka'āinana (The eyes of the land) — Reflects connectedness to the landscape, and the awareness that people are always watching what their leaders do.

Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/

Worksheet 3a: Outline your decision-making activities and cultures

Purpose: Describe how marine-related decisions are typically made within and outside your community

1. Brainstorm, list, and briefly describe all the steps in the research and decision-making processes you are aware of in your area. Indicate which ones are important to your community as well as to external and/or dominant groups. Consider the following questions:

a) How is a problem recognized and defined in your community?

b) How are solutions developed and implemented?

c) What role does data play in the process?

d) Is there follow-up or evaluation?

e) Who is involved? (Note: You will discuss degree of inclusiveness in more detail in Section 4)

f) Is there a difference from the decision-making process of other groups within or outside your community?

g) Are there any unique elements specific to your community context?

2. Discuss the results of the brainstorming

a) How does the list of activities compare with the way you have experienced decision-making in your community?

b) How much agreement is there in your group about how decision-making is made in the community?

c) Is there anything missing?

d) What feels right or wrong?

Sample Worksheet

Worksheet 3b: Identify preferred decision-making processes

Purpose: Decide upon the decision-making culture and key activities you want to incorporate into your D4D plan

Considering the different approaches to decision-making discussed in this section, discuss how you would like decisions to be made.

1. Discuss what decision-making culture should guide your overall planning process

2. Describe the key values and principles of that culture

3. Discuss the activities you listed in Worksheet 3a and list your ideal/preferred activities to include in your decision-making process

4. Reflect on the choices you are making. For example

a) What type of decision-making culture have you settled on? Why?

b) What key decision-making activities have you selected? Why?

c) What activities have you decided not to include? Why?

d) What are possible positive and negative outcomes of these choices?

Sample Worksheet

Describe your preferred decision-making culture

Key decision-making activity

Description Briefly describe what each activity would entail

4

FOSTERING INCLUSION IN THE DECISION-MAKING PROCESS

If representatives from diverse knowledge systems, including scientists and decision makers, accept each other’s legitimacy and power, space is created for developing collaboration from the onset of a project, grounded on the appreciation of different ways of understanding the world. (Maria Tengö and others)1

To do so requires a willingness to embrace the risk inherent in being open to the power of participants, in how one conceives of and delivers support and recognises each other’s voice. It requires taking practical risks in challenging people in their language, roles and attitudes, requiring them to open up their practices and spaces to enable relationships that are flexible, enjoyable and generous with time. It requires engaging hopefully with these risks. (Jonathan Rix and others)2

PURPOSE OF THIS SECTION

This section discusses the importance of providing opportunities for different types of people to participate in the process of generating data for decision-making. It presents examples of models for assessing the existence of marginalized voices in your community.

Results:

1. An assessment of marginalized voices in your community

2. A set of strategies to increase inclusion in decision-making

WO NSA DA MU A | If your hands are in the dish symbol of participatory government, democracy and pluralism

From the adage, “Wo nsa da mu a, wonni nnya wo” — “If your hands are in the dish, people do not eat everything and leave you nothing.”

Source: http://www.adinkra.org/htmls/adinkra/wonsa.htm

1 Maria Tengö et al. (2014, p.585). Connecting diverse knowledge systems for enhanced ecosystem governance: The multiple evidence base approach, MBIO, 43(5), pp. 579-591.

2 Jonathan Rix, et al. (2022, p.152). Taking risks to enable participatory data analysis and dissemination: a research note, Qualitative Research, 22(1) 143–153.

WHOSE VOICE COUNTS

Although community participation in research is not new, it is often poorly or only superficially implemented. Many projects that ascribe to participatory or co-designed research ultimately apply it to limited aspects, primarily including community members as field workers, or sharing results for community validation. Genuine co-design and participation would require relinquishing some power and allowing communities to drive the research agenda at all stages, from defining the problem to evaluating the results. This is not without risk, but it is necessary to start changing the relationship between researchers and the communities in which they work.

Assessing who is and isn’t there. Too often, many groups are not given the opportunity to meaningfully participate in the multiple activities that go into generating data for decision making. Their exclusion may be intentional (from discriminatory bias, or because inclusion seems impractical) or unintentional (due to ignorance or lack of awareness). However, data and the processes that create and use data all emerge from the perspectives and experiences of people. If effectively done, including more

voices leads to more realities being recognized, better methods, more and better data, greater accountability and ultimately better decisions and actions. Therefore, it is important to assess who is and is not included in decisionmaking activities. It is easy to know who is included. However, it can be hard to identify who is marginalized from the process. Two possible approaches to identifying marginalized groups are in terms of their level of participation and representation in decision-making processes. You can use these or other frameworks of your choice to assess community inclusion.

Who participates? It can be helpful to think about typical types of participation and what types of people tend to engage in which ways. The Levels of Participation framework (Figure 4.1) labels a variety of participant types and modes of participation. This can be a starting point to describe who participates in decision-making in your community, how they participate, and whether you would like to see different patterns.

Source: https://www.thepolisblog.org/2010/02/participatory-budgeting.html

Source: Wenger-Trayner.com (n.d.). Levels of participation [Blog Post] https://wenger-trayner.com/resources/slide-forms-of-participation/

Who is represented? The Marginalized Voices Framework (Figure 4.2) provides a structure for identifying five types of voices that are sidelined in data collection and analysis. In addition to highlighting the factors that lead to lack of representation, it provides a basis for developing mitigation strategies. While the framework deals with issues of representation in data collection, it can be adapted to apply to participation in the full range of D4D processes.

For more on the Marginalized Voices Framework:

Figure 4.1: Levels of Participation

1. Unknown voices — people who are invisible from mainstream society and hence invisible to data collection efforts (e.g., uncontacted tribes in the Amazon, modern-day slaves, undocumented migrants). They can be invisible by choice or due to the realities of their lives.

2. Silent voices — people who, due to personal and structural factors, lack the capacity of vocalizing their realities (e.g., houseless populations, elderly living in residences, people with disabilities, children).

3. Muted voices — people who, because of socially-created systems of classification and power, are devalued and discredited (e.g., LGBTQI community, women, people at the bottom of the social hierarchy, sex workers).

4. Unheard voices — people who are excluded from research design and data collection efforts because it is very difficult and costly to reach them using standard data-collection methods and procedures (e.g., people who do not use digital technology, homeless populations, mobile populations without permanent homes, people with low literacy, those who do not speak the language of the country where they live).

5. Ignored voices — people who are marginalized during the analysis of the collected data, both through traditional statistical processes and new data approaches. These are individuals who may fall out of calculated averages or have identities whose complexity cannot be captured by the kinds of data collected (e.g., groups who are categorized as “people of color” or other broad ethnic description, groups who are so small in number that their classifications get grouped in “other”).

Source: Mamello Thinyane (2018). A typological framework for data marginalization. https://i.unu.edu/media/cs.unu.edu/page/4453/UNU-MACAU_Data_Marginalization_Flyer.pdf

Oftentimes, the types of people marginalized from decision-making have similar characteristics in relation to gender, age, expertise, and wealth, amongst other characteristics. For example, women, children, non-scientists, and people with low-income are generally more likely to be excluded than men, adults, scientists, and wealthy people. Assessing who is and isn’t included can start with these common demographic lenses, but depending on the issue at hand, other lenses may be relevant (e.g., physical ability, citizenship, or religious beliefs).

INCLUSIVITY MINDSET

There may be many constraints to getting more voices in the room. Language, technical know-how, gender norms, safety, work commitments and time of day are just some of the many challenges to building inclusive data practices. Addressing these constraints within the limits of what is possible requires both practical and mindset adjustments. The values below can be used to think through how to create inclusive spaces and have an enabling mindset.

Figure 4.2: The Marginalized Voices Framework

What do each of these values mean to you and your community? Are there different or additional values or practices that should be incorporated into your strategy?

Respect each other’s humanity. Recognize the humanity of everyone in the community and acknowledge the relevance of their practices and realities.

ASASE YE DURU | the Earth has weight symbol of providence and the divinity of Mother Earth. represents the importance of the Earth in sustaining life.

Source: http://www.adinkra.org/htmls/adinkra/asas.html

Respect the essence of non-human realms. Allow space for the expression of non-human interests. This is particularly relevant in decision-making on climate change and environmental protection. Accommodate different cultural worldviews on natural and spiritual worlds.

Use an asset-based approach. Recognize and respect what communities already have to offer. Begin discussions with community strengths and visions for the future rather than with problems or deficiencies.

FUNTUMFUNEFU DENKYEMFUNEFU | two mythical crocodiles with one shared stomach symbol of unity in diversity, a common destiny, sharing. From the proverb “Funtumfunafu Denkyemfunafu, wowo yafunu koro nanso wonya biribi a wofom efiri se aduane no de no yete no wo menetwitwie mu” — Funtumfunafu and denkyemfunafu share a stomach but they fight over food because the sweetness of food is felt as it passes through the throat.

Source: https://www.adinkrasymbols.org/symbols/funtumfunefudenkyemfunefu/

Value diversity. Actively recognize, respect, and value the diverse identities within your community. Find ways to express this openly so that all participants feel welcomed and valued.

Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/

Fisher rebuilding canoe on Keta-Kedzikope Beach, Ghana. Photo credit: Kwesi Johnson

What do each of these values mean to you and your community? Are there different or additional values or practices that should be incorporated into your strategy?

DWENINMMEN | horns of a ram represents strength (in mind, body, and soul), humility, wisdom, and learning.

Source: https://www.adinkrasymbols.org/symbols/ dwennimmen/

Be humble. Be open minded; question your reality and how it impacts your assumptions and how you view the world. Consider what power and privilege you bring to conversations, as well as what other participants bring.

Be accountable. Show that you are accountable to the community and indicate how you will follow through with that accountability.

Be flexible. Be open to difference, change and the unexpected. Allow for different meanings and forms of participation. Prioritize inclusion while recognizing the different realities in the community and offer participation options that are feasible for different groups. Accommodate multiple styles of communication for different cultures, languages, and communication abilities. Embrace unexpected developments and find the balance that allows for both completion of objectives and serendipity.

"Time Window" — vision of what a landscape might have looked like before it was overtaken by modern construction and pollution. Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/

CASE STUDY:

Including local communities in D4D on beaching of marine mammals in Ghana

In April 2021, hundreds of marine mammals washed up on the shores of Axim, Ghana. In addition to scientific curiosity about the cause of the deaths of the mammals, there were concerns about people collecting them for sale and consumption. The Ghana Environmental Protection Agency organized a stakeholder meeting to discuss the findings of investigations conducted into the cause of the incidents by four key institutions (the Institute for Environment and Sanitation Studies, University of Ghana; Centre for Coastal Management, University of Cape Coast; Department of Marine and Fisheries Sciences, University of Ghana; and the Fisheries Commission). Invitees to this meeting were primarily high-level officials from five government agencies and research institutions. Though not officially invited, the meeting was attended by researchers from a small community organization (SSRD). SSRD had spearheaded a collaboration with one of the participating universities to obtain eye-witness accounts and investigate community members’ knowledge and perceptions of the beaching of marine mammals. The SSRD team gained entry to the closed-door event by leveraging their relationship with the university. They also intentionally chose to include a community representative who had been a research respondent. During the meeting, it was clear that researchers from the four large institutions had focused primarily on the laboratory examination of the carcasses without giving much consideration to

the local ecological knowledge of the fisherfolk. One of the presenters also reported on community members’ perception that researchers and government officials, in their haste to collect samples and get back to the lab, ignore relevant community knowledge. Drawing on their own fieldwork, SSRD and the community member were able to complement the lab research findings with local ecological knowledge. As a firsthand eyewitness, the community member clarified some information such as the estimated number and state of the beached mammals — principally that, contrary to news reports, the mammals were still alive when they washed ashore. He also shared his knowledge of previous instances where pods of whales were seen to be moving away soon after possible noise pollution at their habitat.* An additional observation was that, whether accurately or not, people in the affected communities perceive the mass beaching of different species of fishes to be a perennial occurrence and a boon for the fishing community. The meeting participants ultimately decided that the lab results were largely inconclusive and suggested that other possibilities such as offshore anthropogenic sources should be considered.

* For example, from military exercises or offshore oil and gas mining.

This case study is based on a research collaboration between SSRD and the Ocean Nexus “Inclusive Ocean Data for Decision-Making” project. Co-written with Edwin Boachie-Yiadom, Theophilus Boachie-Yiadom and Kwesi Johnson.

Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/

MANY SHAPES OF INCLUSION

The reality is that it is easier to participate in some D4D activities than in others. Many people will not have the time, energy, interest, or skills to participate in all stages. But that should not preclude their voices from being heard. Where direct participation is not possible, think of ways to achieve representation - to identify and incorporate (or at the very least acknowledge) the perspectives of those not participating.

It is important to have a good understanding of the community and of the scientists and/or policymakers involved to prepare for possible obstacles to community participation. These could include:

• Socio-political barriers such as community reluctance or lack of interest misaligned government and governance structures, obscure communication styles of scientists, and social inequity.

• Resource limitations including financial, human resources, time and infrastructure

• Physical processes such as natural hazards5

It is also important to recognize that participation does not always result in representation. The choices made during a decision-making process might not include the preferences expressed by some participants. This could be because of the need to compromise due to conflicting views, or because some participants had more power to influence the process. Taking time for reflexivity at different points in the process will provide opportunities to identify where representation is not being achieved, why and what to do about it.

Planning for an inclusive D4D process requires searching for innovative ways to get more voices and realities to the table and into the actual decisions.

Consider this: What practical issues do you need to account for to foster inclusive decision-making in your community?

ACTIVITY: Building an inclusive group

In this activity, you will assess the extent to which voices in your community are or are not included in typical decision-making processes and identify strategies to foster inclusion.

• Assess marginalized voices and develop inclusion strategies in Worksheets 4a, 4b and 4c.

What are the barriers to successful community-based climate

Environment, 24, 1–17. https://doi.org/10.1080/13549839.2019.1580688

5 Annah Piggott-McKellar, Karen McNamara, Patrick Nunn, & James Watson. (2019, p.24).

change adaptation? A review of grey literature. Local

The Menehune are a magical race of miniature people noted for their ability to construct massive stone edifices. They embody the balance of power and humility. Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/

Worksheet 4a: Assess the voices in your community

Purpose: Assess which voices are typically included or excluded from your decision-making processes, decide which voices need to be included, and how that can be achieved

1. Use tools like the Levels of Participation and Marginalized Voices Frameworks to think about the different groups in the community, their levels of inclusion and marginalization, and the reasons for these trends. Allow for the possibility of identifying groups that were not apparent to you at first. Revisit and update this worksheet periodically.

2. Review the marginalized and included voices you have identified and discuss:

a) Are there differences in who is and is not included in each step of the D4D process? Why? What does this tell you about your decision-making culture?

b) For groups that are included in certain decision-making stages, why? At what points are they included? Is their level of inclusion appropriate (e.g., too high, too low, just enough)? What systems or practices enable their inclusion? What are challenges to maintaining their inclusion?

c) For groups that are not included in certain decision-making steps, why? At what points are they marginalized? What systems or practices contribute to their marginalization? What are challenges to increasing inclusion?

3. Discuss whether there are differences in the types of people or groups that are included and excluded in each step. Why? What does this tell you about your decision-making culture? (Refer back to the choices you made about your decision-making culture and update as necessary.)

Sample Worksheet

Included only in identifying issues in fish marketing – because most fish sellers are women and there is a Fish Sellers Association. Not included in any other stages. Should have input on fishing methods, data collection and data analysis.

MARGINALIZED

Example: Migrants Fully

Nature of marginalization.

Prohibited from owning fishing boats. Lack of legal framework on economic opportunities for migrants makes it easy to overlook them. Should have input on fishing methods and working conditions on boats.

Worksheet 4b: Identify necessary voices

Purpose: Decide which voices need to be included.

Discuss and decide on which societal groups should participate in each D4D step and why Sample Worksheet

Worksheet 4c: Develop inclusion strategies

Purpose: Decide how inclusion of specific groups will be achieved

1. List all D4D activities you included in Worksheet 3b and discuss what strategies you could use to facilitate participation and representation of necessary groups.

2. Reflect on the challenges you might face. E.g.,

a) What challenges might you face in including the different voices you described in the previous section?

b) Are any challenges related to the power differences between you and those groups?

c) How could you try to address this?

Sample Worksheet

Example: Define the issue that needs a decision

For women – 1) ensure at least 25% of D4D planning committee members are women; 2) if that is difficult to achieve, ensure that a committee member attends a Fish Sellers Association meeting to request input in advance of D4D planning meeting.

A sightless giant renowned as a deep listener, great healer, sharer of wisdom and medicine to support the health of communities and island resources. In response, communities use the knowledge shared to protect the health of both the human and natural worlds. Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/

5

WAYS OF KNOWING AND INCLUSIVE D4D GUIDING PRINCIPLES

Two-Eyed Seeing refers to the mindful effort of learning to see from our one eye with the strengths of the Indigenous knowledges and ways of knowing while also learning to see from our other eye with the strengths of the Western (or mainstream, or Eurocentric, or conventional) scientific knowledges and ways of knowing and, furthermore, to mindful efforts towards using them together in our contemporary academic programs and community endeavours. Furthermore, Two-Eyed Seeing needs to be understood as often a “weaving back and forth between” the perspectives represented… and not domination or assimilation. (Cheryl Bartlett)1

PURPOSE OF THIS SECTION

In Section 3, we discussed decision-making cultures — these affect the process of decision-making. In Section 5, we discuss guiding principles for the content of decisionmaking plans.

Results

1. A description of your views on how knowledge is created

2. A list of principles to guide the content of your D4D plan

HOW DO YOU KNOW WHAT YOU KNOW?

Using data for decision-making is essentially a process of trying to know something so that you can decide what to do about, for, or with it. This seems straightforward until you consider that different cultures and academic disciplines have deeply-held beliefs and values regarding

knowledge, reality, and truth. Stemming from this are different ways of understanding and responding to the world around us. Whether based on gut instinct, seasoned experience, faith in longstanding tradition or a preference for tangible data, these “ways of knowing” are directly linked to people’s ideas about what constitutes appropriate evidence (data) upon which to base a decision. One culture or tradition might insist on being able to see and touch something before it can be considered evidence; or that many people must have seen, heard, or said something in order for it to be true. Other cultures or traditions might

KURONTI NE AKWAMU | Kuronti and Akwamu

two groups that together form the council of a town or village. symbol of democracy, sharing ideas, and taking counsel.

Source: https://www.adinkrasymbols.org/symbols/kuronti-ne-akwamu/

1 Cheryl Bartlett. (2005, p.73). Knowledge inclusivity: “Two-eyed seeing” for science for the 21st Century. Learning communities as a tool in natural resource management: Proceedings of a workshop Held in Halifax, Nova Scotia, 70–76.

insist that things that cannot be seen or touched should still be considered evidence, or that the views of a small number or particular types of people are sufficient to represent truth.

GUIDING PRINCIPLES FOR INCLUSIVE WAYS OF KNOWING

Explicitly outlining your ways of knowing in comparison to that of other stakeholders in your community is one way to raise consciousness about alternative worldviews and reflect on your openness to other knowledge systems. We define D4D guiding principles as the ideas about knowledge and participation that you want to be reflected in the actual content of your D4D plan. If, for example, you consider western science to be more credible than indigenous science, your decision-making plan is likely to give higher priority to data derived through western methodologies, and lower priority to data from indigenous approaches. Or, if non-binary gender identities are considered important in your community, you will probably make a conscious attempt to capture them as a demographic group when designing your data collection tools. It is important to reflect on these attitudes and the extent to which they might enhance or constrain the inclusiveness of your decision-making plan.

Some ways of knowing align well across contexts, but some do not. For instance, intuitive ways of knowing might not be compatible with western standards of objectivity or validity. If ways of knowing are diametrically opposed, it can be extremely difficult to collaborate on finding data to address an issue. Nevertheless, for inclusive decision–making, it is important to try to figure out how to work with different ways of knowing. The extent to which your D4D plan is able to accommodate contrasting value systems could be a testament to how inclusive you can be.

Consider this: One group of stakeholders believes that male household heads can speak on behalf of the family, while another group believes only individual household members can speak about their reality. Can both perspectives be incorporated into a D4D project on climate change impacts?

ACTIVITY 5.1: Explore different ways of knowing

• Examine ways of knowing in Worksheet 5a

ACTIVITY 5.2: Define guiding principles for the content of your D4D plan

• Define your D4D guiding principles in Worksheet 5b

Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/

CASE STUDY: UNDERSTANDING THE ENVIRONMENT THROUGH TRADITIONAL KNOWLEDGE AND RESEARCH EVIDENCE

After the beaching of dolphins in Axim, Ghana (see Case Study in Section 4), researchers from SSRD and the University of Cape Coast interviewed fishers and other community members in the area about their experience of the event. Several interviewees sought to debunk the information circulating that the dolphins were dead when they washed up, noting that the stronger ones were able to return to sea: “we actually witnessed the dolphins being beached, so we are telling you the real story of how it all happened. They were not dead; we even took videos of the dolphins for future reference” (Fisher1).

Among the reasons proffered for the beaching, fishers mentioned potential ocean water changes: “Adom nsuo” (peace water) and/or “nsuo boni” (bad water), described as “dirty,” “starchy,” sticky, “very hot” and having “a particular smell.” They implied that this water could kill or disorient fish and marine mammals, but not necessarily make them unfit for consumption. At the same time, although one respondent suggested there were changes in the water and a lot of seaweed that day, most respondents could not state categorically that these water changes had been present before or at the time of the beaching: “it is difficult to say what actually caused it, because everyone is giving his or her own perspective about the beaching of the

dolphins. But what I have learnt from the elderly fishermen is that it was caused as a result of changes in the water in the sea... ” (Fisher2)

Whereas the focus of scientists and law enforcement agents was on testing the dolphin bodies directly and prohibiting people from collecting and selling them for fear of health consequences, community members argued that scientists and policy enforcers could have taken alternative measures to balance fishers and sellers’ livelihood needs with public health and conservation needs. Suggested alternatives included testing the water at the time of the beaching to determine if water temperature, pollution or other change was present, and keeping the dolphins alive or storing (instead of burying) the dead ones while testing was done to determine if they were safe to eat. Explaining why no water samples were taken, one interviewer explained that the ocean conditions had been remotely captured using satellite technology. It is not clear whether the findings of that satellite data were shared with the community.

1. What ways of knowing are being exhibited by scientists, fishers and other community members?

2. Could these ways of knowing be woven together to understand the beaching of the dolphins?

3. What benefits could be derived from weaving them together?

4. What challenges could be faced in trying to weave them together?

This case study is based on a research collaboration between SSRD and the Ocean Nexus “Inclusive Data for Decision-Making” project. Quotes are translated from the local language. Co-written with Edwin Boachie-Yiadom, Theophilus Boachie-Yiadom and Kwesi Johnson.

Worksheet 5a: Explore ways of knowing

Purpose: Reflect on how you make decisions about knowledge, reality, and truth (If necessary, bring in experts on different ways of knowing to support the activity.)

Sample activity

1. Consider the following statements:

• Climate change is real

• Human beings are causing climate change

• Fishing with explosives is harmful to the ocean environment.

• Closed fishing season should begin in March every year

2. For each statement, discuss the following:

• Do you think the statement is true or untrue?

• Why do you think it is true or untrue? What knowledge did you use to come that decision?

• Where did you get the knowledge that you used to come to that decision?

• How would you go about convincing another community member about your point of view?

3. On sticky notes write down:

• The types of information that you used to make your decision

• The sources of that information

4. On flipcharts, place your sticky notes where you think they best fit in these ways of knowing:

• Intuition — I feel or do not feel it or I just know it

• Authority — a leader or someone I trust told or has not told me

• Logic — I have or have not thought it through based on accepted principles of logic

• Observation — I have seen (or not seen)/experienced (or not experienced) it myself

• Scientific — I have systematically gathered and analyzed (or not) information on it

• Are there other ways of knowing in your community that are not captured by these five categories?

5. Discuss:

• What ways of knowing seem to be dominant and why?

• Which ways of knowing seem to be subordinate or excluded and why?

• What is the effect of this?

• Who holds knowledge in your community?

• How do you respond when someone outside your community presents different types of information that contradict your opinion on what is true or false?

Worksheet 5b: Define your D4D guiding principles

Purpose: Create a set of principles about knowledge creation to guide the content of your D4D plan

1. List and briefly describe the guiding principles that you want to incorporate into the decision-making plan. Consider the following questions:

a) Are the guiding principles unique/important to your community only or to others as well?

b) Have you captured perspectives from all voices defined in your community?

c) How flexible or rigid do you want your D4D guiding principles to be?

d) Does everyone in the group have the same understanding of each principle?

e) Do any of the principles contradict each other? Does it matter?

f) Are there any principles that will be hard to implement, or might cause discomfort to some participants?

g) If there is a relatively powerful group in your decision-making environment, do you think they could benefit from adopting any of the unique principles of your community?

h) Do you think your community could benefit from adopting any of the unique principles of the more powerful group?

2. After discussion and reflection, revise the list as necessary and decide which principles to incorporate.

3. Consider the following questions about the process:

a) Participation — which societal groups will participate in defining the principles that should be reflected in the D4D plan? Why? What can be done to broaden participation?

b) Representation — whose ways of knowing are covered and/or prioritized in the guiding principles? Why? What can be done to broaden representation/mitigate lack of representation?

c) What are the implications of non-participation and/or non-representation?

Sample Worksheet GUIDING

Example: We are guided by scientific evidence. All activities must be geared towards producing measurable proof.

DEFINING THE ISSUE 6

How are the research questions asked? Are they deficit-based questions that rely on a deficit assumption about the researched, or do the questions focus on strengths and positive images of the researched… Does the research problematize and critique the tendency to make the researched speak through the voices, academic language, concepts, and theories of the West? (Bagele Chilisa)1

PURPOSE OF THIS SECTION

When crafting a decision-making plan, it helps to take some time to clearly define the issue you want to address. This process can be time consuming, especially when attempting to do so in an inclusive manner. But it is important, as it drives everything that follows. This section presents basic steps for selecting the D4D issue and outlines some considerations to keep in mind to make the choice as inclusive as possible.

Results:

1. Selection of the issue or issues for which a D4D plan will be made.

2. Assessment of whose interests are and are not addressed in the framing of the issue.

HWEHWEMUDUA | rod of investigation or measuring rod

symbol of excellence, superior quality, perfection, knowledge, and critical examination.

Source: https://www.adinkrasymbols.org/symbols/hwehwemudua/

DEFINING THE ISSUES FROM THE COMMUNITY PERSPECTIVE

Starting with a positive orientation

The process of using data for decision-making will typically begin with acknowledging an issue on which a decision is needed. Oftentimes in the context of climate change or other environmental topics, this issue is a “problem” — something that some people (e.g., community leaders, scientists, policymakers) believe needs to be fixed. Although scientists and policymakers generally refer to this as a “research problem”, the issue does not have to be defined from a negative standpoint. There are increasing calls for alternative approaches to framing topics when doing community-based research, such as asset-based or desire-centered methods. Defining the issue in terms of community assets or strengths is a way of being accountable, respecting the dignity of the community and shifting away from the tendency to cast the community as either victim or culprit. Examples of approaches that propose this stance are participatory appraisal, participatory action research and appreciative inquiry.

FOR MORE INFORMATION ON:

Participatory action research:

Appreciative inquiry :

Including community voices

Your organization likely already has an agenda that informs your work and underpins your D4D planning. The issue might be easily defined based on first-hand experience (such as noticing the disappearance of some fish species), or information from trusted sources (such as campaigns about the need to adapt coastal habitats in preparation for rising sea levels). Or sometimes the issue might be unclear and require additional effort to figure out how best to characterize it (such as having a general interest in understanding how global warming might affect the community). Whatever the scenario, it is important to define the specific issues in consultation with the relevant communities. This will help to confirm community interest

in the same issues or might reveal competing interests or priorities. Striving to be inclusive means being open to revising your ideas about what the most relevant or important issues are. It also requires actively advocating for the interests and needs of marginalized voices within the community to be heard and incorporated into defining the issue.

Consider this: What tools and techniques are most appropriate for engaging with the community to define the issues? Will these techniques exclude any relevant community voices? What strategies will you suggest, if necessary, to broaden participation and representation?

Gathering of fisherfolks after landing of fish at Kedzikope-Keta, Ghana.

Photo by Philip Prah

DEFINING THE ISSUE

The way a D4D issue is defined can have a significant effect on the quality and usefulness of the data that is collected, as well as the likelihood that communities will support decisions made with that data.

Defining the issue usually has three main components:

1. Defining the topic broadly. This is the issue you want to address, solve, or investigate. For example, “coastal erosion.”

2. Articulating why the topic is important. This could include describing how it affects the community, what could happen if it continues or ends, or how the community could benefit from addressing the issue. Explaining why the issue is important can also reveal whose interests are being prioritized, subordinated, or sidelined. For example,

- “Coastal erosion is an important issue to address because it is destroying beach biodiversity” — highlights the interests of nature conservationists.

- “Coastal erosion is an important issue to address because it is destroying oceanfront properties” — highlights the interests of property owners or commercial entities.

3. Narrowing down to one or two questions to focus on. These “research questions” express the identified topic more narrowly, usually in the form of a question. You might be interested in finding out why the issue exists, who is most affected by it, or how it is changing. For example, “What are the main causes of coastal erosion in this city?” or “Which forms of marine life are most affected by coastal erosion?”

The selected issue and questions could emerge from a variety of sources, such as:

- a future-state that the community wants to achieve (e.g., become a tourist attraction)

- a practical problem for which the community wants to find a solution (e.g., level of plastics pollution)

- an issue that the community is or should be concerned about (e.g., ocean acidification)

- an observation that the community is curious about or wants to understand (e.g., beaching of marine mammals)

- an issue the community disagrees on (e.g., use of prohibited fishing methods)

Some topics might be easier to research than others. Think about whether it will be possible to gather data on the issue, given your resource and time constraints, as well as the nature of the issue itself (for example, is it something that people would be reticent to talk about?).

Because an inclusive process will likely generate a multitude of potential issues representing the interests of different people, you will have to decide which one to focus on. This will require negotiation amongst participants. Having some selection criteria can help to make a choice.

Selection criteria could include:

- urgency of the issue

- importance of the issue

- popularity of the topic

- cost of researching that issue (e.g., financial, time, expertise)

- research-ability of the issue

NKYIMU | the crossed divisions made on adinkra cloth before stamping symbol of skillfulness, precision

Before adinkra cloth is stamped with the symbols, the artisan blocks off the cloth with lines in a rectangular grid using a broadtooth comb. This preparation is symbolic of the exacting technique which results in the highest quality product.

Source: http://www.adinkra.org/htmls/adinkra/nkyimu.htm

Consider this: What is your goal? Do you want to Describe something? Explain something? Predict something? Evaluate something? Your research questions should reflect this goal.

Other things to consider:

- Define the issue clearly. The clearer the issue is, the easier it will be to determine how to find data on it, design the data collection methods, and use the data. For example, “the impact of closed season on fishers’ annual income” is clearer than “the impact of closed season on the fishing industry.”

- Consider different ways of framing the issue to ensure you are asking questions that are more likely to generate useful and possibly novel insights. Let different people

CASE STUDY: DEFINING A D4D ISSUE

Following the beaching of fish in Accra in April 2021, a local research team with strong communityrelationships sought to contribute knowledge to the decision-making process. This is how they defined the issue.

Broad area: Beaching of fish and marine mammals

Why it is important: There has been recent washing ashore of some fish species and marine mammals (all in the month of April 2021) at various points along Ghana’s 560km coastline. These incidences have caused a lot of fear and panic nationwide, and a lot of speculation amongst stakeholders with experts and non-experts all posturing opinion, concerns, and suggestions. The regulatory agencies including Fisheries Commission of Ministry of Fisheries and Aquaculture Development (MoFAD) and the Environmental Protection Agency (EPA) have cautioned the public not to consume such fish and marine mammals, as there is no scientific evidence yet proving the cause of death. Samples have been taken by competent scientists for necropsy. However, there is an urgent need to understand local perceptions and knowledge on such occurrences, whether there are any relationships culturally and how their socio-economics play a role or are affected by these occurrences. For SSRD and partners, it is important to consider the fact that

women are one key component in driving the market. Gender is intricately woven into the supply of fish in Ghana and neighboring countries. Understanding the local ecological knowledge is key to ensure a platform is created to promote active participation by locals in decisionmaking that is based on ocean data.

Narrowed down focus:

a. The knowledge of local communities on beaching of marine mammals at Axim in the Nzema-East Municipal Assembly of the Western Region.

b. The role of the sexes (gender) in rescue efforts and taking the mammals home for consumption and processing for the market.

1. How clearly is the issue defined?

2. What is the goal of the research, and is it reflected in the narrowed down focus?

3. In what other ways could the issue have been defined and what difference would it make?

4. Does this framing avoid defining the issue from a deficit perspective?

describe the issue in their own words. For example, “fishers’ perception of closed season policies” is likely to produce different types of results than “fishers’ perception of fairness in implementation of closed season policies.”

- Seek simplicity but acknowledge complexity. Frame the issue as simply as possible but do not ignore complex aspects if they are relevant.

- Be willing and prepared to revise the definition and/or questions during the research design process. Despite appearing to be linear in nature, the research process is usually highly iterative.

- Explore questions that are of interest to both the community and policymakers.

- Check the assumptions underlying the way you describe the issue and related questions. What does your framing imply about how you view the issue? Or whose viewpoint does it capture? Try framing the issue from a different person’s viewpoint.

Sample resource on defining the issue:

ACTIVITY: DEFINING THE ISSUE

In this activity you will decide what topic you want to address with data. Define the issue in Worksheet 6

This case study is based on a research collaboration between SSRD and the Ocean Nexus “Inclusive Data for Decision-Making” project. Co-written with Edwin Boachie-Yiadom, Theophilus Boachie-Yiadom and Kwesi Johnson.

Worksheet 6: Define the issue

Purpose: Determine the issue on which data is needed and what aspects to collect data on

1. Brainstorm to create a list of possible issues to focus on. E.g.,

a) What environmental issues are of concern in your community?

b) Why are they considered to be a problem, and by whom?

c) Prioritize the issues — Which one(s) do you want to make a D4D plan for, and why?

2. Select the topic and explain why it is important.

3. Brainstorm again to identify and select research questions for the selected topic.

4. Reflect on the level of participation and representation – Who participated in the process? Whose problems are covered and/or prioritized in the problem definition? What (if anything) can be done to broaden representation and mitigate lack of representation? What are the implications of non-participation and non-representation?

Sample Worksheets

TOPIC

Why is it a concern?

Who is concerned about the issue? (e.g., fishers, farmers, marketers, policymakers)

TOPIC

SELECTED TOPIC

Prioritization criteria (e.g. urgent, important, popular, less costly, interesting, more researchable)

Why is it important?

Research questions

IDENTIFYING DATA NEEDS 7

PURPOSE OF THIS SECTION

Having determined the issue you want to make a decision on, the next step is to figure out what specific kinds of information will best help to make the decision. This section covers some basic concepts related to information that is gathered for decision-making, and principles for identifying your information needs.

Results:

1. A list of the data you need to collect to understand the issue

2. Assessment of how inclusive your chosen data will be

WHAT IS “DATA”?

Data is any information that enables us to understand issues and make decisions. It is not just numbers: data can take different forms, including numbers, words, sounds, and pictures. For example, a number for the air temperature, a word or picture for the color of seaweed,

or words describing people’s perceptions of the risks of environmental pollution. Different types of data could be used to describe the same phenomenon (e.g., color or temperature to determine when a piece of fish is cooked). However, people have different views about which types of data are better, appropriate, or more accurate.

What does the word “data” mean to you? In what ways is this similar and different to others? How has your view been shaped by your experiences?

Two of the most common ways in which researchers think about data are according to who collected the data (primary or secondary data) and according to whether the data can be expressed in numbers (quantitative or qualitative data).

NEA ONNIM | One who does not know from the Akan proverb, “Nea onnim no sua a ohu” –When one who does not know learns, they get to know.

Source: https://www.adinkrasymbols.org/symbols/nea-onnim/

Drawing upon diverse knowledge systems expands the evidence-base, increases legitimacy, and builds trust for decision making and environmental management (Steven Alexander and others)1

1 Steven Alexander et al. (2019, p.21). Bridging Indigenous and science-based knowledge in coastal and marine research, monitoring, and management in Canada, Environmental Evidence, 8(36), pp.1-24.

Primary and secondary data. Primary data is information that has been collected directly by the researcher, from an original source. For example, if you wanted to know how new fishing regulations were impacting your community, you could ask community members what their experiences have been. Secondary data, on the other hand, is information that was not directly collected by the researcher. For example, with the same question about fishing regulations, you could look at interview or survey data that was collected by someone else but is accessible to you, such as through a database; or you could examine historical logs of fish catches in the area documented by the government.

Quantitative and qualitative data. Quantitative data is information that can be tangibly counted or measured, such as the number of a certain kind of fish, the height of a tree, or the temperature of water. Qualitative data is information that cannot be captured or expressed as a number (or quantified). Qualitative data is non-numerical, often descriptive information such as the color of the ocean, the smell of the beach, or an opinion on how good or bad fishing regulations are. Historically, western approaches to environmental issues have tended to value the more tangible quantitative data over qualitative. However, both types have merit and there is a growing shift that recognizes a mix of quantitative and qualitative measures. The specific issue you are addressing should ultimately determine the kinds of data used.

Are there other ways you think about data in your community?

Indicators and Indices

Because there are many possible ways to measure something, you will have to decide what specific

measurement to use. Indicators are the information selected to serve as a measure of a particular item or issue. For example, the color of a lake could be taken as an indicator of its overall health. Indicators can be qualitative or quantitative, and a single issue may be measured with multiple indicators in order to capture different dimensions. For example, the number of a certain species of fish could be an indicator of how well fishing management policies are working. However, this says more about the impact on fish populations, and not much about the impact on human populations. If fish stocks are rising, but so is hunger because local people are not allowed to fish, it may be determined that management policies are in fact not working. Depending on the decision-making goals, one may need to include multiple indicators to account for both natural and human environments.

To make analysis more manageable, multiple indicators are sometimes combined into a single index or a set of indices. Indices enable scientists to describe, assess and compare complex environmental systems in relatively simple terms. For example, the Ocean Health index is used to evaluate the relative health of oceans around the world “comprehensively and quantitatively.” 2 Several other indices exist, covering issues from fish stocks to climate change. 3 The simplicity of an index, however, has the potential to overshadow unique local variations and deprioritize certain types of knowledge and realities, thereby significantly decreasing the accuracy of measurements. Most indexes give more weight to quantitative measurement and using western approaches. If locally-valued data does not fit the model used to create an index, it will likely be excluded. Not only does this decrease representation; it also has the potential to make transfer of findings to other communities unreliable, essentially defeating one of the key goals of creating indices in the first place.4

2 https://ohi-science.org

3 https://www.fisheries.noaa.gov/insight/ocean-atmosphere-climate-indices

Tasting well water to test the salinity level after flooding of coastal areas, Volta region, Ghana.

Photo by Philip Prah

CHOOSING YOUR DATA AND INDICATORS

The data you choose to collect should directly connect to the issue you are to address, but it is not always a straightforward decision. Suppose you decide to collect data on the economic impact of changes in fish stocks over the last five years. Thinking about the two main parts of the issue: 1) economic impacts and 2) changes in fish stocks, you might list the items in Table 7.1 as your possible indicators. Which of them would best enable you to understand the economic impact of changes in fish stocks?

• Total community revenue

• Average HH income

• Average income of fishing families

• Number of businesses startups or closures

• Community members’ confidence in their future financial stability

Sometimes, circumstances, such as a limited budget will force choices of fewer indicators than you would like. How will you make those choices? Given that no data plan can be perfect, we should be transparent about the limitations that shape our choices. Even where there are no constraints, it is crucial to be realistic with what you need to collect versus what you can collect. While we might be tempted to get as much data as possible, collecting too much data can result in becoming overwhelmed and unable to figure out how to use the data, or in collecting data that never gets analyzed. All the indicators in Table 7.1 could be useful and interesting, but do you need them all? To avoid collecting unnecessary data, ask questions like “What will we do with the data? Exactly how will we use it?”; “How much time or money will it take to collect the data?”; “How essential is this data to answer our research questions?” If you continue to really want to collect a

• Total fish counted

• Health of fish

• Average size of fish

• Color of fish

• Average time needed to catch certain amounts of fish

• Community perceptions on health of fish

certain kind of data that does not tie clearly to your research question, it can be useful to revisit the research question and see if it should be revised. Perhaps the question does not really represent what you want.

ASSESSING THE QUALITY OF DATA

Considering that the use of data in decision-making has real life impacts, it is important that any data used is the appropriate data and is used appropriately. Data quality refers to the extent to which data is suited to its purpose. Poor quality data is likely to lead to poor quality decisions.5 Data could be considered good or bad quality depending on how it is acquired and used. However, defining “quality” is not simple. Researchers have identified a vast number of criteria for data quality (Figure 7.1).

Table 7.1: Potential indicators of economic impacts and fish stock levels

ECONOMIC IMPACTS CHANGES IN FISH STOCKS

4 Shinya Hosokawa, Kyosuke Momota, Anthony A. Chariton, Ryoji Naito, & Yoshiyuki Nakamura. (2021). The use of diversity indices for local assessment of marine sediment quality. Scientific Reports, 11(1), 14991–14991. https://doi.org/10.1038/s41598-021-94636-0

5 Monique Kilkenny, & Kerin Robinson. (2018). Data quality: “Garbage in – garbage out”, Health Information Management Journal, 47(3), pp. 103-105. https://doi.org/10.1177/1833358318774357

Source: Li Cai, & Yangyong Zhu. (2015, p.4). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14(2), pp. 1-10, DOI: http://dx.doi.org/10.5334/dsj-2015-002

BEING INCLUSIVE IN CHOOSING DATA AND INDICATORS

The data and indicators you select should be developed as inclusively as possible. As with other decisionmaking processes, selecting data should be approached dynamically while keeping an eye on feasibility. As more voices are included, data needs can evolve — more voices could mean more kinds of data to collect. However, limited time and resources often necessitates prioritizing some data over others. This challenging task of prioritizing and deprioritizing data must be done collectively. Without adequate consultation, the prioritized data may only represent the interests of those with the most power. Routinely assess data choices to check if they might marginalize any groups, either substantively or as it relates to their worldviews.

Data quality: The kinds of data we value and prioritize reflect our values and cultures. Expanding participation and representation is the only way to capture the collective knowledge of different historically validated approaches to data. For example, while Western science often teaches that “good” data is neutral and should only present objective “facts,” other approaches, such as indigenous science, hold that data is not neutral but is rather shaped by the individual researcher and their relationship with the environment or community. There are many who argue that significant value is added to research when we accept our personal relationship to the research; it can add

commitment, thoughtfulness, and accountability to do things right. In any project, the data collected is the result of choices made by people. As noted by Eber Hamption, “feeling, living, breathing, thinking humans…do research. When we try to cut ourselves off at the neck and pretend an objectivity that does not exist in the human world, we become dangerous, to ourselves first, and then to the people around us.”6 Because researchers embody the experiences, histories, realities, and other lenses that make them who they are, approaching research as neutral removes the vital human connections and relationships that enable societies to function together.

People will be unwilling to accept decisions that were based on data that they view as poor quality data. Therefore, it is essential to consider whether the community agrees with your definition of good quality data. Being open to different criteria could also make it easier to question the quality of your own data, acknowledge its limitations, and create space for other types of data to fill the gaps. Adopt a reflexive stance to avoid inadvertently imposing your definition of quality on the community.

BOA ME NA ME MMOA WO | Help me and let me help you symbol of cooperation and interdependence

Source: http://www.adinkra.org/htmls/adinkra/boame.htm

6 Cited in Shawn Wilson. (2008, p.56). Research Is ceremony: Indigenous Research Methods. Halifax: Fernwood Publishing.

Figure 7.1: Sample data quality criteria

How do you judge the quality of data? Are there other ways of assessing the quality of data in your community?

CASE STUDY:

HEALTH INDICATORS FOR CARIBOU HERD MANAGEMENT

Caribou are one of the most important economic and food animals in the Arctic and subarctic areas of North America. Thus, caribou management and conservation has long been an important issue for indigenous and non-indigenous groups. Herds are heavily influenced by hunting patterns and the severity of winters, and known to travel across vast geographic areas. Developing systems to monitor the number and health of caribou requires thoughtfully-developed data systems. Based on generations of experience hunting caribou, the Cree people have developed indicators of herd size and health. These indicators are quite different from the kinds of indicators typically used by Western scientists.

Western systems of management tend to rely on quantitative measures, such as population survey data or movements measured with GPS tracking devices, for scientific monitoring. On the other hand, the monitoring system of the Cree, ”neither produces nor uses quantitative measures. Rather, using a qualitative assessment of caribou fat, it provides a mental model that gives hunters an indication of the population trend over time, along with the relative health of the animals… It does not require the quantitative estimation of the population size itself for making decisions.“ (p.150)

Caribou fat is a crucial indicator that “integrates the effects of a number of environmental factors, such as environmental stresses and range conditions, acting on the caribou populations” (p.150). Such traditional knowledge and approaches are thus valid and appropriate for measuring herd health, and can complement Western approaches. At the same time, in some situations, such as when caribou are affected by successive bad winters, using fat as a health indicator could be incomplete and should be complemented by other indicators for effective management decisions.

1. How would a quantitative indicator, like number of herd, enable the Cree to make decisions about the management of the caribou resource?

2. How would a qualitative indicator, like fat content, enable the Cree to make decisions about the management of the caribou resource?

3. What other indicators could be used when caribou fat alone is not an adequate measure?

4. Could the Cree method of qualitatively assessing caribou fat be used by Western scientists?

5. Could Western quantitative methods be used by the Cree?

6. What types of indicators could be combined into a system that uses both Western and Cree indicators?

Data ownership and privacy. Being inclusive in this context also means being respectful and thinking critically about what it means to collect data from or about particular people and communities. For instance, it is important to consider issues of ownership and privacy. A lot of knowledge is culturally connected or has economic value to a community. Researchers may wittingly or unwittingly collect and use such data in ways that are disrespectful or harmful to those from whom it was collected. Many communities have developed protocols to guide appropriate collection and use of their information, reinforcing the idea of indigenous data sovereignty, which maintains that indigenous groups have an inherent right to govern all aspects of data about their communities and environments. It is important to follow established protocols and make sure permission is granted before data is collected and used.

Additionally, thinking inclusively requires accounting for different perspectives on privacy. Whenever data is collected, stored, and used, there is the risk that the data could be seen or used by others who do not have permission. This could result in economic, physical, emotional, or other kinds of harm. Yet societies might not necessarily think of privacy in the same way — e.g., one community might place high value on personal privacy, while another might not. Concerns about data privacy are further heightened as digital technologies enable more automatic, invisible, and pervasive forms of data collection. A data plan should indicate clearly both how it will protect data privacy and accommodate community members’ views on privacy, if they differ.

This case is adapted from Berkes. (2018). Sacred ecology (Fourth edition.). Routledge.

Consider this: In a study exploring the rejuvenation of native seaweed, your community needs to decide on a set of data to measure the impact of seaweed management policies. One local group proposes two types of data: 1) the change in the amount of seaweed in the bay and 2) community satisfaction with management practices.

• What indicators could be used to measure each type of data? For example, how can the amount of seaweed be measured? (e.g., number of plants, seaweed or ocean color, health of certain fish species, presence of seaweed in the digestive track of certain fish species)

• What social, economic, environmental, or political dimensions should be captured?

ACTIVITY: Decide on data and indicators

• Are the two types of data sufficient to give an accurate picture of “impact”?

• Which community members would you collect data from to measure “community satisfaction”?

• How might different groups approach this differently?

• What different types of insights would quantitative and qualitative data yield?

• Who benefits from different kinds of data and indicators being included?

• Could there be any data ownership or privacy concerns with collecting these types of data?

In this activity you will choose the specific types of information you want to collect.

• Determine your data and indicator needs in Worksheet 7

Kiritimati Island. Coastal fisher and partner… the role of women in fisheries is not always valued but is always there.

Photo by Franscisco Blaha, www.franciscoblaha.info/photography/kiritimati-island

Worksheet 7: Determine the types of data and indicators you need

Purpose: List the data needed to understand the issue and the specific indicators you will collect

1. Form small groups or work individually.

2. Revisit the issue/research question.

3. List around 5 of the most important types of data that would enable you to understand the issue you are addressing.

4. For each kind of data, briefly indicate

a. If it is qualitative or quantitative

b. If it is primary or secondary

c. How important it is to understand the issue

d. Why it is important

5. Consider the following questions:

a. How much does your list represent others in the room?

b. How much does your list represent others in the community outside of the room?

6. Go around the room with each individual/group sharing their lists. Create a master set of data representing all ideas from the group. (Before combining items from different groups, make sure they are referring to the same thing.)

7. Discuss and adjust the list to arrive at your final set of indicators

POSSIBLE ACTIVITY FOR DISCUSSING THE LIST.

• Make a space in the room for everyone to stand

• Split that space in half using a physical or visual designation. One side should be labeled as “Yes”. The other as “No.”

• For each of the data listed, ask the following questions. Once each question is read, participants need to move to either the “yes” or “no” side. A middle “it depends” could also be considered. After each question is read and participants choose their sides. Some individuals on each side should share why they chose what they did. They are also allowed to ask clarifying questions, as well as change sides if the discussion leads them to think differently.

- Did you have this data in your original list?

- Is this data necessary?

- Is it possible to collect? (discuss as a group what methods that could be used to collect the data)

- Would this data represent the situation, needs or interests of a typically excluded group?

DISCUSSION QUESTIONS

1. How similar or different are the lists of each group or person?

2. What types of data dominated — qualitative, quantitative, or a mix?

3. What research cultures are these kinds of data associated with? Should there be data from other cultures on the list?

4. Which kinds of data do you personally want to use?

- What values do they represent?

- What do or would different groups within your community think about this list of data?

- Which members of your community would be represented in that data? Which ones would not?

- What ways of knowing are prioritized in the types of data and indicators you have selected? Why?

- What types of issues and whose realities, interests and needs would be captured by the data and what types would not? Whose problems are covered and/or prioritized in the data selected? What are the implications of this?

- What can be done to broaden representation or mitigate lack of representation?

5. How might your position in relation to the community affect the choices of data you make?

6. Which societal groups are participating in deciding what data you need, and which are not? What are the implications of this? What can be done to broaden representation or mitigate lack of representation

7. Revisit your research questions

- Would all the data directly answer the research question? For any that would not, why?

- Would the data address issues important to the whole community or mutually agreed on by the whole community?

- Revise either the research questions or the data choices to ensure that they align with your overall purpose as a community.

CHOOSING DATA COLLECTION METHODS 8

Knowledge and peoples will cease to be objectified when researchers fulfill their role in the research relationship through their methodology …a lot of people have tried to decolonize research methods. But they are deconstructing a method without looking at its underlying beliefs (Shawn Wilson)1

... combining scientific and local methods for monitoring wildlife provides, on the one hand, an opportunity for customary users to scrutinize science and, on the other hand, for science to learn about relationships and processes previously unknown (Maria Tengö and others)2

PURPOSE OF THIS SECTION

This section discusses the process of selecting methods for collecting your data, and how to consider inclusiveness at this stage.

Results:

1. Selection and description of your data collection method

2. Assessment of how inclusive your chosen method is

WOFORO DUA PA A | when you climb a good tree symbol of support, cooperation, and encouragement

From the expression “Woforo dua pa a, na yepia wo” – When you climb a good tree, you are given a push.

Source: http://www.adinkra.org/htmls/adinkra/wofo.htm

1 Shawn Wilson. (2001, p.177). What is an indigenous methodology? Canadian Journal of Native Education, 25(2), pp.175-179.

2 Maria Tengö, Eduardo Brondizio, Thomas Elmqvist, Pernilla Malmer, & Marja Spierenburg. (2014, p.585). Connecting diverse knowledge systems for enhanced ecosystem governance: The multiple evidence base approach, AMBIO, 43(5), pp.579-591.

TYPES OF DATA COLLECTION METHODS

Once you have identified the types of data and indicators you need for your decision-making, you can now determine how to collect the data — this is your research or data collection method. The overarching strategy and rationale for how you carry out the research is your research methodology, and the specific procedures you use are your research methods. 3 In this section, we focus on research methods, while noting that the values associated with your overarching methodology will have an impact on the inclusiveness of your methods. To demonstrate the existence of radically different approaches to research, we frequently compare “western” and “indigenous” (Box 8.1) methods; although, these are not the only ways to categorize research approaches.

Box 8.1: Indigenous methodology

“An Indigenous methodology is a research approach by and for Indigenous peoples, using techniques and methods drawn from their millenarian cultures and traditions. In Indigenous methodology, the process of research is more than the production of new knowledge. It entails pedagogical, political, moral, and ethical principles that resist oppression and reflect the unique knowledge, beliefs, and values of Indigenous communities. This methodology allows Indigenous people to make their own decisions about research question/topic and research processes without any outside interference. It gives to Indigenous communities control over their ways of knowing and the development of Indigenous knowledge. For non-Indigenous researchers, an Indigenous methodology allows the researcher to enter into the world alongside Indigenous experience rather than framing the Indigenous world-view from a distance.”

Source: Cameron et al, 2014, p.E5. Understanding inequalities in access to health care services for Aboriginal people: A call for nursing action, Advances in Nursing Science, 37(3), pp. E1-E16.

The term ‘research method’ tends to be associated with western science, but it can in fact include any of the ways we gather information to generate our knowledge, whether indigenous, local, or foreign; whether formal or informal. Methods can utilize any of our senses, including sight, sound, smell, touch, and taste; or might use machines such as computers or phones. They may involve going out to collect data in a physical location (fieldwork), collecting information from people virtually (internet research), or accessing information from offline or online documents (desk research).

Researchers generally differentiate two basic types of methods depending on whether their focus is on quantitative and qualitative data (Table 8.1). Quantitative methods collect data that is countable and usually produces numbers or graphs. Qualitative methods collect data that is describes experiences and meaning, and usually produces words or images. A third approachmixed methods - combines quantitative and qualitative elements. For decision-making, western marine science tends to regard quantitative methods as providing stronger data than qualitative methods. However, there is increasing acknowledgement that qualitative research, with its more context-based orientation, might be more appropriate in many cases. In general, although non-western science also uses quantitative methods,4 it seems to align more with qualitative research methods.

3 For more on research methodology, research methods and related concepts, see: Viknesh Andiappan & Yoke Kin Wan. (2020). Distinguishing approach, methodology, method, procedure and technique in process systems engineering, Clean Technologies and Environmental Policy, 22(3), pp. 547-555. https://doi.org/10.1007/s10098-020-01819-w

4 See for example: Hayward et al. (2021). Addressing the need for indigenous and decolonized quantitative research methods in Canada, SSMPopulation Health, 15, 100899; Walter & Anderson (2013). Indigenous statistics: A quantitative research methodology, Left Coast Press; Blackstock, (2009). First Nations children count: Enveloping quantitative research in an indigenous envelope, First Peoples Child & Family Review, 4(2), 135-143.

Interviewing community members at Agavedzi Beach, Ghana. Photo by Kwesi Johnson

Table 8.1: Some differences between qualitative and quantitative methods

Qualitative methods

Data in words or images

Focus on describing things

Seeks depth

Usually intended to be context-specific

Usually has a small sample size

Thematic, interpretive analysis

Generally viewed as “subjective”

The range of possible research methods, western and non-western, includes case studies, experiments, focus group discussions, interviews, observation, participant observation, photovoice, specimen analysis, surveys, symbol-based reflection, talking circles, yarning.5 There are many ways to justify the method you choose but every choice comes with embedded beliefs and values about knowledge, reality, and truth (see section on Ways of Knowing). For example, a preference for a survey that counts things could indicate that you believe all phenomena can be quantified, or that only quantifiable issues matter. A researcher who believes that unquantifiable things do matter, might favor methods like interviews that do not depend solely on counting things. Western science research generally emphasizes values of objectivity (the researcher is expected to be dispassionate about the research and have no personal influence on the inputs, process, or results); validity (accuracy of your measurements) and reliability (consistency of your measurements). These values are generally easier to achieve with quantitative research methods. In contrast, indigenous research accepts (and even expects) subjectivity (the researcher is personally invested in the project), and prioritizes relationships (with the community and environment in which the research is done) and accountability (for the process and impacts of the research).6 These values might be easier to pursue with qualitative research methods. However, it is also possible to combine approaches from different methodological traditions.7

Quantitative methods

Data in numbers

Focus on measuring or predicting things

Seeks breadth

Usually intended to be generalizable beyond context

Usually has a large sample size

Statistical analysis

Generally viewed as “objective”

Whatever method you choose, it is important to understand and acknowledge the values and beliefs that underpin it.

5 For examples, see Ranjan Datta. (2018). Traditional storytelling: An effective Indigenous research methodology and its implications for environmental research. AlterNative: An International Journal of Indigenous Peoples, 14(1), 35–44. https://doi.org/10.1177/1177180117741351; Amy Wright, Olive Wahoush, Marilyn Ballantyne, Chelsea Gabel, & Susan Jack. (2016). Qualitative health research involving indigenous peoples: Culturally appropriate data collection methods. The Qualitative Report, 21(12), 2230–2245. https://doi.org/10.46743/2160-3715/2016.2384

6 For more on indigenous research methods, see: Chilisa Bagele. (2019). Indigenous research methodologies (2nd Edition). SAGE Publications; and Shawn Wilson. (2001). What is an indigenous methodology? Canadian Journal of Native Education, 25(2), pp.175-179.

7 See for example, Sarah Hunt, & Nancy Young. (2021). Blending indigenous sharing circle and western focus group methodologies for the study of indigenous children’s health: A systematic review. International Journal of Qualitative Methods, 20, 16094069211015112. https://doi. org/10.1177/16094069211015112; Rhiannon Martel, Matthew Shepherd, & Felicity Goodyear-Smith. (2022). He awa whiria — A “braided river”: An indigenous Māori approach to mixed methods research. Journal of Mixed Methods Research, 16(1), 17–33. https://doi.org/10.1177/1558689820984028

Waves permeate through all things. Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/

What types of methods do you prefer? What values do they represent? In what ways are they appropriate for your community and purpose?

BEING INCLUSIVE IN DATA COLLECTION METHODS

Choosing your method: As with other aspects of your plan, your methods should be as inclusive as possible. They should involve members of the affected community, respect values and traditions in the community context, and be adaptable to developments in the field. Too often, data collection is seen as an easy way to incorporate community participation by leveraging community members’ knowledge of households and the environment or simply having access to relatively cheap labor. To be truly inclusive, however, you should strive to include the community in the process of choosing a data collection method and in the actual data collection. This requires time for the researcher and the community to educate each other on the research methods they are familiar with and/or prefer. The earlier you start engaging with the community, the more likely you are to arrive at a method that is appropriate, successful and does not cause harm.

In western science, choosing your data collection method typically includes describing your specific method, sample, questions, and tools. Other research traditions require different or additional elements such as positionality statements (a statement on the researcher’s identity and relation to the research topic and context).

Research purpose: Your choice of method should be shaped by the issue you have defined and the type of data or indicators you are seeking (see Sections on Defining the Issue and Identifying Data Needs). For example, if you have decided to collect data on the economic impact of changes in fish stocks over the last five years, your method might include some of the following:

- go to the national marine data collection institution and request records on the quantity of fish caught in your region over the last five years.

- a survey of fishers in the community to ask them how much fish they caught over the last five years.

- storytelling sessions with households in the community to talk about how their lives over the last five years.

As you review the possible methods, consider what type of information/insights each one might miss. For example, depending on pre-existing information means your information might be a bit outdated, the institution does not collect and process marine data frequently. Or, in societies where there are no female fishers, collecting data on fish catch or from fishers only would exclude the views of women who might be more involved in the marketing and financing aspects of the fishing industry. You will have to decide whether to accept the limitations of your chosen method (for example based on a limited research budget) or do something to address them. Either way, you should make a conscious effort to acknowledge the limitations and their implications for the knowledge you produce.

Researcher’s purpose: The way you view your role and accountability as a researcher should also guide your methods, but the community’s expectation of your role might be even more important. If your role is to be relatively detached from the community, this might mean choosing methods that enable you to collect data in a detached manner (e.g., observation rather than participant observation). If your role is that of a community advocate, this might mean choosing methods that enable community immersion.

The method you choose and how you implement it will affect how the community views you. Additionally, you will not always be able to control your role — some aspects will be chosen for you in ways that might be invisible to you. For better or worse, communities will respond to you differently or have different expectations of you, based for example on your socio-economic background, gender, or their colonial and postcolonial history with your home country. Being aware of the power dynamics and historical relationship between yourself and the community can help you to acknowledge the impact of past and current injustices and take steps to address power imbalances as much as possible in your research.8

8 For more on researcher reflexivity and positionality, see for example: Andrew Holmes. (2020). Researcher positionality — A consideration of its influence and place in qualitative research. A New Researcher Guide. Shanlax International Journal of Education, 8(4), pp. 1-10. DOI: https://doi.org/10.34293/ education.v8i4.3232; Stephen Secules, Cassandra McCall, Joel Alejandro Mejia, Chanel Beebe, Adam Masters, Matilde Sánchez-Peña, Martina Svyantek. (2021). Positionality practices and dimensions of impact on equity research: A collaborative inquiry and call to the community, Journal of Engineering Education, 110(1), pp. 19-43. https://doi.org/10.1002/jee.20377

CASE STUDY: TESTED OR UNTESTED SURVEY QUESTIONS?

In the development of the research instruments, we found it challenging to balance between using psychometrically validated instrumentation and tools that were meaningful to the local community. Psychometrically tested measures are desirable from a research perspective because the validity and reliability of the questions have been tested. We found it challenging to use existing questions and scales that captured variables we were interested in measuring. Existing instruments were difficult to find that used geographically appropriate Spanish and/or had been tested among Mexicans/Mexican Americans. Even the measures that did fit those criteria did not always make sense to our CHWs* and participants. The solution, which was guided by the ISF, was to do cognitive interviews with the CHWs and modify the questions that were absolutely necessary as slightly as possible based on the findings.

For example, the Patient Health Questionnaire depression module had been translated and tested with individuals in Honduras and was found to be an efficient and reliable screening measure. We determined through the cognitive interviews that the response category for the last question contained terminology that was confusing for our population and needed to be changed slightly.

Excerpt from Allison Hopkins and others (2016, p.431). It’s complicated: Negotiating between traditional research and community-based participatory research in a translational study. Progress in Community Health Partnerships: Research, Education and Action, 10(3), pp. 425-433.

* Community Health Workers

1. Do you think the “quality” of the data collected will be affected by the modification of questions? Does it matter?

2. What alternative actions could the team have taken?

An indigenous researcher’s view on the researcher’s role

“As a researcher you are answering to all your relations when you are doing research… So your methodology has to ask different questions: rather than asking about validity or reliability, you are asking how am I fulfilling my role in this relationship? What are my obligations in this relationship?” (Source: Shawn Wilson. (2001, p.177). What is an indigenous research methodology? Canadian Journal of Native Education, 25(2), pp.175-179.)

Appropriateness: The method should also be appropriate for the context in which it will be used.9 For example, in some cultures, it may not be appropriate to interview people about certain topics in certain locations. Historical misrepresentation of communities in survey data might mean that this method no longer works in those communities. Cultural values of relationship-building might make ethnographies or participant observation more acceptable methods than rapid surveys. Equally important are your fieldwork processes — e.g., how and when you approach potential participants, who will go where or talk with whom, what community protocols you will need to respect, and what permissions/agreements you need to secure. These decisions shape what types

of data can be collected, from whom or where, who can participate in collecting the data, and what can be done with the data collected.

The specific tools or techniques you use to capture data — e.g., pen and paper, tablets, camera, tape measure, push button, satellite technology, ocean salinity refractometer — may seem straightforward, but also come with implications. Avoid choosing a method just because it is considered innovative or “cool.” This is especially important when considering the use of new technologies. Technology is a powerful tool that has enabled significant new ways of understanding our environments. But it has also been used in ways that, intentionally or unintentionally, are abusive, exploitative, and harmful to people. Understanding the appropriateness of technological data collection tools is important to protect the environment, the community, and your relationships.

Research skills: Different methods and their associated tools require different skill sets — for example, knowing how to use a particular piece of scientific equipment, how to swim to collect underwater samples, or having the ability to enter certain household or social spaces. Your choice of method could determine who can and cannot participate in collecting the data. It can also influence

9 Amy Wright, Olive Wahoush, Marilyn Ballantyne, Chelsea Gabel, & Susan Jack. (2016). Qualitative health research involving indigenous peoples: Culturally appropriate data collection methods. The Qualitative Report, 21(12), 2230–2245. https://doi.org/10.46743/2160-3715/2016.2384

whose perspectives and realities get represented in the data. For example, on some topics, a male interviewer might not get frank responses from female interviewees —to be truly inclusive, you would need to find or train female interviewers. Being inclusive in this aspect of your research requires balancing the technical requirements of different methods and the goal of enabling non-technical

Consider this: In a study exploring the impact of ocean acidification, you have chosen to do either an onlinebased household survey or to directly measure the health of oysters using scientific equipment. In both cases, community members will help with the data collection.

- Are there potentially important social, economic, or political dimensions that would not be captured by these methods?

- Would qualitative research methods yield different types of insights?

- Will measuring the health of marine organisms give you an adequate picture of the impact of ocean acidification?

people to participate in the process. This does not negate the value of the training and experience held by experts. However, decisions on how and when to use different types of expertise should be made in association with members of the community.

- Will you survey household heads only? What are the benefits and limitations of this?

- What would the use of an online method mean for people who have limited or no internet access?

- Do you see a role for digital technology in your data collection?

- Does the use of scientific instruments exclude people from participating in data collection?

- Who benefits the most from community members participating in data collection?

- Are community members being used as cheap/ convenient labor or are they being included in other aspects of the research?

ACTIVITY: Select methods for collecting the data

you need.

In this activity you will assess and make choices from the variety of potential data collection methods available to you.

• Choose your data collection method in Worksheet 8

Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/

Worksheet 8: Choose your data collection method

Purpose: Identify and choose from possible data collection methods

1. Based on the data needs you identified in Worksheet 7, list and describe the methods you could use for data collection. Include whatever elements of methodology are appropriate or required for your context and community (See sample tables below).

2. For each one, assess the extent to which it would produce the data you need. What are the strengths and limitations of each method from a research standpoint and an inclusion standpoint?

3. Revisit research questions and, if necessary, revise either the research questions or the methods to ensure that they align with your overall purpose as a community.

4. Decide which method(s) you will use.

5. Reflect on participation and representation — which societal groups have or will participate in deciding what methods you will use, and which will not? Whose problems are covered and/or prioritized in the selected methods? What are the implications of non-participation and non-representation? What can be done to broaden participation/representation and mitigate lack of participation/representation?

Potential discussion questions

1. What types of methods have you listed? What research traditions are these methods associated with? Are there methods from other cultures that could be on the list?

2. What are the benefits and limitations of each approach in terms of your research values and in terms of enabling greater inclusion?

3. Which method do you want to use?

• What does this indicate about your values?

• What do or would different groups within your community think about that method?

• Which members of your community would be able participate in implementing that method?

• Which members of your community would be represented in the data you collect using that method?

• How might your answers to these questions be different if you choose a different method?

• What tradeoffs are you willing to make?

4. How might your position in relation to the community affect the choice and implementation of the method? What can you do about it?

5. Are there any groups not in the room today whose participation might have caused this conversation to go differently?

6. How does having more voices in the room help to consider and prioritize different methods?

7. Does the discussion of methods indicate that it might not be feasible to collect some of the data you want? If so, what could be done in its place?

Sample Worksheets

• Method — E.g., survey, storytelling.

• Sample population the pool of people, things or spaces you will collect data from.

• Sample size — the number of people, things or spaces you will collect data from.

• Questions — Specific questions you will ask or information you will collect.

• Tools and processes — Mechanisms you will you use to collect data/do fieldwork.

• Relationality — How you will build and maintain relationships within the research environment.

• Positionality — How your identity, values, status, privilege etc. in relation to the research context might influence the process and results of the research.

9

DECIDING ON DATA ANALYSIS APPROACH

While for some the meaning of the data comes from the patterns and regularities which emerge as a consequence of data sorting, for others it is a more creative and imaginative activity. (Mary Maynard)1

In the context of participatory feminist research conducted directly with girls and women, who controls the analysis and interpretation of the results? The desired democratization of the relationship between researchers and participants must, logically, be reflected in the analysis process. (Myriam Gervais and others)2

PURPOSE OF THIS SECTION

This section discusses the challenges of doing inclusive data analysis in the research process. Although we discuss data analysis as a distinct stage that follows data collection, note that data analysis can be an ongoing and integral part of the entire research process, not something that happens only after data collection.

Results

1. Description of how you will make sense of the data collected

2. Assessment of how inclusive your data analysis approach is

DONO NTOASO | the double drum two tension talking drums joined together. symbol of strength and unity.

Source: https://yaraafricanfabrics.com/wp-content/ uploads/2014/07/dono-ntoaso_african-symbol.jpg

1 Mary Maynard. (2004, p.1). Feminist issues in data analysis. In Melissa Hardy & Alan Bryman, Handbook of data analysis (pp. 131–145). SAGE Publications, Ltd. https://doi.org/10.4135/9781848608184.n6

2 Myriam Gervais, Sandra Weber, & Caroline Caron. (2018, p.21). Guide to participatory feminist research. McGill University. https://uqo.ca/docs/21778

DATA ANALYSIS AS A (POLITICAL) PROCESS

“Data analysis “involves goals; relationships; decision making; and ideas, in addition to working with the actual data itself… ” (Migrant and Seasonal Head Start Technical Assistance Center)3

Data collection often results in a lot of information that can be difficult to absorb. Data analysis helps to make sense of the data you have collected by organizing it in particular ways, ascribing meaning to the organized data, and drawing conclusions that answer your research question.

The way you analyze data is tightly linked to your research values. Some research traditions expect data analysis to be a fairly rigid process with clear boundaries and techniques; others allow it to be a fluid and iterative process that evolves alongside other research activities. For instance, researchers can start a study with predetermined expectations (hypotheses) about what the results will be and will use deductive data analysis processes to test whether their hypotheses were right or wrong. Conversely, the researcher can choose not to have any predetermined expectations; in which case they would use inductive data analysis processes to see what patterns and trends can be found in the data and then make conclusions based on those observations. In contrast to Western approaches, Tyson Yunkaporta & Donna Moodie4 assert that “Indigenous Knowledge is only valid if it is produced in groups or pairs — individual analysis is considered to be invalid and lacking intellectual rigour.”

Does either inductive or deductive data analysis appeal to you or align with the issue you are researching? Why or why not? If neither appeals to you, how do you think about data analysis as a research activity? How could taking a particular approach help or hinder your purpose?

Your institutional and/or social vested interests can also affect data analysis and interpretation. The same data can be used to reach completely different conclusions when it is intentionally or unintentionally analyzed in ways that produce results that are favorable or unfavorable to different groups. For example, leaving extremely high or low responses (outliers) out of the analysis, or combining less frequent answers into a generic “Other” category can make data analysis easier and the results more representative of the typical participant. But it can also mean that the views of small, marginalized groups are erased or made invisible. The issues you highlight and how you highlight them signal priorities and, sometimes, biases that can privilege or punish particular groups. Thus, it is important to continue applying a critical and questioning lens to decisions about data analysis to ensure the process does not lead to or worsen social inequities.

BEING INCLUSIVE IN DATA ANALYSIS

From the typical researcher’s perspective, data analysis is the domain of the researcher, and often done separately from the research participants or the research site. Even if the researcher’s goal is to represent the voices of participants in their own words, the conclusions reached are those of the researcher and different from what research participants would conclude.

Furthermore, different people, groups, or communities might be interested in analyzing different things. The researcher might want to analyze the relationship between fishing methods that use explosives and the presence of harmful chemicals in the fish population, while women in the community might be more interested in knowing whether their methods of smoking fish can reduce the level of harmful chemicals.

Being inclusive in data analysis means adjusting one’s view of whose domain data analysis is and allowing room for others to participate in making sense of data

3 Migrant and Seasonal Head Start Technical Assistance Center. (2006, p.11). Introduction to data analysis handbook. AED/TAC-12. https://files.eric.ed.gov/fulltext/ED536788.pdf.

4 Tyson Yunkaporta & Donna Moodie. (2021, p.89). Thought ritual: An Indigenous data analysis method for research. In T. McKenna, D. Moodie, & P. Onesta (Eds.), Indigenous Knowledges: Privileging Our Voices (pp. 87–96). Brill Sense. https://doi.org/10.1163/9789004461642

“Early on in the development of feminist debates about research, there was criticism of potentially exploitative practices where a researcher, powerful because of her position, invests nothing of herself in a project, while expecting other women to speak freely and frankly. Such power is also present in the analyzing of data, when the respondents are absent and the researchers’ decisions about

interpretation are paramount. How might it be possible to guard against reading unwarranted meanings into texts or to ensure that appropriate connections are made between them?” (Mary Maynard 2004. pp.140-141, Feminist issues in data analysis. In Melissa Hardy & Alan Bryman (Eds.) Handbook of data analysis (pp.131-145). London: Sage. https://doi.org/10.4135/9781848608184)

How similar or different are the techniques below from those used for making sense of data in your community?

Sample techniques for making sense of data

Statistical or quant analysis

Interested in

Frequencies: How often?

Averages: How typical?

Standard deviations: How much variation?

Regression analysis: What relationships?

Results presented as

Tables, charts, calculations, etc.

Content analysis

Themes: What issues?

Sentiment analysis: What feelings?

Narrative analysis: What meanings? What stories?

Discourse analysis: What language?

Tables, charts, word clouds, prose, etc.

Visualization

Network analysis: What types of relationships? Diagrams, etc.

Thought ritual Connection: Connectedness (researcher with other relationships in the research)

Diversity: Diversity within the relationship

Interaction: Flows of energy, matter and information

Adaptation: Revelations and ancestral connections

Photos, drawings, artwork, etc.

Table 9.1: Examples of Data Analysis Techniques

and generating knowledge that is relevant to them. For this to happen, you might have to adjust your data analysis techniques, invest in training others to apply your techniques and/or invest in training yourself to apply other people’s data analysis techniques.

Depending on your methods, data analysis can be simple and easily undertaken by anyone, it can be very technical and require specialized skills, or it can be highly contextual and require fluency in the local culture. Your choice of data analysis method will necessarily exclude some people either from participating in the analysis or from fully understanding the results from a technical standpoint. Where there is trust in the research relationships, this should not be a problem. In some cases, community members can be trained to collaborate on data analysis. But sometimes it could be that it is scientists who that would benefit from training on local understandings and approaches to data analysis.

CASE STUDY: DOING DATA ANALYSIS IN A PARTICIPATORY RESEARCH PROJECT

During a particularly awkward moment, S. C. asked the CAB, “Why is it so hard to analyze these interviews?”

An elder explained that the interview transcripts were difficult to read and understand, and that the themes were confusing because when making themes, everything became scattered. She said that Crow people don’t break things apart.

The meeting shifted. CAB members became engaged in the conversation and the energy returned to the room. CAB members emphasized that for Crow people, storytelling is a way of honoring tradition and honoring ways of knowing. They said that everything Crow people do has a story behind it and people share their experiences as a way of teaching others. They shared that having scattered categories and breaking apart people’s stories loses the meaning and the understanding of the whole picture and purpose of the story. Moreover, it felt like a violation of the Crow culture because there is always a bigger purpose of the story that is lost when it is broken up into themes. Another CAB member explained, “Crow people work with words using stories, not by breaking stories apart.”

They also expressed that analyzing by breaking apart felt disrespectful to the women who shared their stories and

For example, western methods (both qualitative and quantitative) tend to view analysis as a process of examining the parts of a whole; breaking down issues into component parts that can be isolated from each other and from their source. To make sense of data, they use techniques such as calculating frequencies, averages, or standard deviations; identifying themes or patterns; or mapping out connections. These approaches might not align well with other research traditions or certain local contexts; for example, where understanding the whole is considered more important than understanding the parts (see Case Study). Similarly, methods of presenting the analyzed data can inadvertently inhibit participation or reflect particular ways of knowing. Frequency tables, pie charts, statistical calculations, word clouds, drawings, pictures, or even just prose, all have the potential to influence what aspects of the results are communicated and how easy or difficult it is for people to understand them.

that the story’s impact hinges upon the experiences and relationships the storyteller has to those receiving the story. For example, when a respected elder shares her experiences, it is very impactful to her audience in large part because of who is speaking. In keeping with Western scientific methods, the interview transcripts were coded anonymously, not mentioning the names of the women who shared stories. CAB members explained that when the elder is not named, the person receiving the story loses their connection with the elder, thus losing an essential part of the impact of the story.

Toward the end of the fourth meeting, team members decided to take time to think about the stories and how the team could use stories to understand the transcripts. After the meeting, V. W. S., S. C., and the Crow Project Coordinator discussed the team’s struggles. Given the CAB’s feedback, the PRECEDE-PROCEED model would no longer be used to organize the themes. However, the next steps for analysis remained unclear.

This excerpt describes a project that sought to do participatory data analysis and was proceeding successfully until the data analysis stage.

1. What does this experience demonstrate about the things you need to think about when trying to do data analysis in an inclusive manner?

2. Do you have any suggestions for how the team could proceed with data analysis in this scenario?

Excerpt from Vanessa Simonds & Suzanne Christopher. (2013, p. 2187). Adapting Western Research Methods to Indigenous Ways of Knowing, American Journal of Public Health, 103, pp. 2185-2192.

http://www.franciscoblaha.info/photography/2019/12/13/marshall-island

SAMPLE QUESTIONS FOR SCIENTISTS TO ASK DURING DATA ANALYSIS

• “Am I averaging my results? Would I obtain different conclusions if I averaged a different way?

• Am I reporting results for some regions or time periods in favor of others? Would I obtain different conclusions if I made different choices?

• How am I choosing to plot my results? Would a reader’s conclusions change if I plotted the results in a different way?

• On what sources of evidence am I relying? Are there other sources of evidence? Would I obtain different conclusions if I used different sources of evidence? How am I reporting confidence in my results?

• What results am I choosing to report? Would a different scientist, perhaps from a different location or demographic, report different results, or would they report the same results differently?”

Ben Kravitz & Tina Sikka (2021, p.17). Conducting more inclusive solar geoengineering research: A feminist science framework, arXiv, https://arxiv.org/ftp/arxiv/papers/2109/2109.04217.pdf

ACTIVITY: CHOOSE A DATA ANALYSIS APPROACH

In this activity you will decide how to analyze the data you collect.

• Decide on your approach to data analysis in Worksheet 10

Analyzing fishing data from maps, logbooks, catch logsheets and temperature records on Marshall Island.

Photo by Francisco Blaha,

Worksheet 9: Decide on your data analysis approach

Purpose: to decide how you will analyze the data you collect

1. Discuss how you are going to make sense of the data you have collected. List the analysis methods and techniques you will use and how you will present the results.

2. Identify those among you who already have some expertise in data analysis of any kind.

3. Identify other potential experts in data analysis, especially from different research/knowledge traditions.

4. Discuss the different data analysis approaches or techniques. Which would be appropriate and/or preferable for your data.

a. What are commonalities and differences in the approaches?

b. How well do they align with ways of sensemaking in the community?

c. Would some community members be able to participate in these forms of analysis? Which ones? Does it matter if they cannot? How important is it for community members to be able to participate?

d. Would scientists be able to participate in the community’s ways of sensemaking? Does it matter if they cannot? How important is it for scientists to be able to participate?

e. What are the benefits and limitations of each approach in terms of your research values and in terms of enabling greater inclusion? What tradeoffs are you willing to make?

5. Reflect on participation and representation - Which societal groups will participate, and which groups will not participate in 1) deciding the data analysis approach and 2) conducting the data analysis itself? Why? What can be done to expand participation? What points of view/agendas are captured and/or prioritized in how the data will be analyzed, interpreted and presented? Whose realities, interests and needs are reflected and whose are not? What can be done to broaden representation or mitigate lack of representation?

10

DECIDING HOW DECISIONS WILL BE MADE (AND IMPLEMENTED)

... even if participation was “good”, it may not be capable of handling the complexities of science, management, time and space that integrated coastal redesign will demand… there may be a disjunction between the “mood of the people” and the application of precautionary science. This may require a more interactive process between sciencebased management and stakeholders, possibly facilitated by training. (Tim O’Riordan)1

PURPOSE OF THIS SECTION

This section prepares you to think about what types of issues to consider when connecting your data to strategies for action and/or policy. It also prompts reflection on how you will implement decisions arising out of the data analysis and the possible roles of affected communities in that process.

Results:

1. A description of how you will make and implement a decision on the issue

2. Assessment of how inclusive the chosen process will be

NKYINKYIM | twisting symbol of initiative, dynamism and versatility

Source: http://www.adinkra.org/htmls/adinkra/nkyi.htm

1 Tim O’Riordan. (2006, p.174). Inclusive and community participation in the coastal zone: Opportunities and dangers. In Jan Vermaat et al. (Eds.): Managing European Coasts: Past, Present, and Future (pp. 173–184). Springer-Verlag Berlin Heidelberg.

BEING INCLUSIVE IN DECISION-MAKING AND IMPLEMENTATION

From the beginning of this resource, we have emphasized that data-based decision-making includes the entire process of generating the data that is used to make the decisions. If that mindset has guided your work, then by the time the actual decision is to be made, you should have incorporated perspectives from a diversity of community representatives, even if it is not perfect. Making and implementing the decision could then be simply a matter of revisiting the agreements you have all negotiated along the way. However, even at this stage, it is possible to neglect or deprioritize the needs of particular groups.

When defining the issues that needed data, you might already have had some possible courses of action in mind; and were viewing the data collection as a way to make a decision on which one to choose. In this scenario, your next steps might be relatively straightforward, especially if you have also agreed on the criteria by which to decide which conclusion will lead to which action. For instance, if the level of ocean acidification is above x%, then you will relocate all oyster farms; or if fish stocks have fallen below y%, then you will implement a closed fishing season. Alternatively, you might have been viewing the data collection as a way to understand the issue in order to identify and select a course of action. Meaning there will be more work to do to identify and evaluate policy and action options. That process itself might require more data collection.

Even where options are already outlined, new data could bring to light other considerations that require you to adapt the original options and criteria. Let’s say your data shows that a closed fishing season will have negligible impacts on two communities but catastrophic impacts on a third. The community most negatively affected should ideally have a voice in the actual decision to move forward with this option, whether to voice their protest or to discuss possible alternatives or modifications to the planned decision.

The range of decision options considered, as well as what are seen as reasonable options, also reflect different realities and have different implications. Whereas a closed fishing season might seem like a reasonable option for long-term ocean health, this perspective might be viewed differently by the artisanal fisher whose livelihood revolves entirely around fishing in a particular area versus the industrial trawler that might be able to easily move to a different location. When faced with the actual decision, people may feel differently about how acceptable it is.

Similarly, the way a decision is implemented can make or break its effectiveness. Will the strategy focus on compliance or enforcement? Will communities be receptive to the type of people tasked with enforcing the decision on the ground? Are other necessary systems in place to enable communities comply with the decision?

QUESTIONS TO ASK YOURSELF

• How transparent will the final decision-making point be?

• Now that the decision is to be made, how do/will people feel about it?

• Who will communicate the decision and how?

• Who will implement and monitor compliance (if needed)?

• Is it feasible to implement the decision (based on human, financial, and infrastructure resources)? If not, what are alternative courses of action?

• Will the measure have a greater or worse chance of success if community members participate in its implementation?

ACTIVITY: DECIDE HOW DECISIONS WILL BE MADE AND IMPLEMENTED

In this activity you will outline a plan for how you will use the data you collect to make a decision, and how you will implement the decision.

• Decide how you will make and implement decisions in Worksheet 11

CASE STUDY: DATA AND KNOWLEDGE FOR DECISION-MAKING ON THE PROTECTION OF DOLPHINS

Stakeholders used different lines of reasoning to make decisions on how to treat the beached dolphins in Axim, as well as on the broader issue of dolphin/human relations (see Case Studies in Sections 4 and 5). Some community members were aware that dolphins are a protected species in Ghana, while others indicated that they were not aware, or did not understand why dolphins are protected. With the key point of contention being the directive that the beached dolphins should not be captured, sold, or eaten, opinions varied as to whether the dolphins were alive and healthy or dead and potentially poisonous when they beached.

Among community residents, both fishers and nonfishers drew on their direct observation of the state of the beached dolphins (alive when they beached), information from seasoned fishers, their knowledge of the ocean (change in the water), behaviors of dolphins at sea, and the high market value of the dolphins. Their decision was to capitalize on the opportunity of a windfall harvest, resist calls not to capture and sell the dolphins, and reject offers of below-market-price compensation for returning any dolphins they had already collected.

Scientists focused on collecting samples of the dead dolphins for lab analysis, as well as using oceanographic data collected via satellite. They also appear to have relied on hearsay reports that the dolphins washed up dead, hence the heightened concerns about their potential consumption. Their decision was to seek answers to why the dolphins beached or died, discourage people from consuming them and explore community perspectives on the event. It is unclear whether they subsequently updated community members about whether the dolphins were indeed unsafe to eat.

Policy makers and implementers were guided by the same types of data as scientists, but there are examples of how they also drew on their tacit knowledge about livelihood issues in the community. Their decision was a combination of coercive deterrence (using the police force) and persuasion (rhetorical and financial). In persuasion mode, they tried to reason with community members and in one case, offered to purchase the dolphins that people had captured (though at below the expected market price). They also supported sample and environmental analysis using Western scientific methods.

Ultimately, there was resentment against decision-makers, especially those perceived as being from outside the fishing community — fishers expressed frustration at lost income opportunities; distrust of the motives of the politicians and scientists who had intervened to prevent sale and consumption of the dolphins; and noted that the situation could have been handled differently to accommodate the needs of the fishing community.

These sentiments were reiterated at a series of community awareness raising events several months after the beaching, at which participants recounted grievances against dolphins that routinely enter or get caught in their nets and feed on the catch, as well as against government authorities and conservationists for interfering with the communities’ main source of livelihood. Questions were raised about what should be done if a dolphin gets caught and/or dies in a fisher’s net. Fishers suggested that they should be allowed to keep and sell it, rather than release or dump it back into the ocean as is currently required. In response to concerns about fishers’ potentially misrepresenting that they did not kill the dolphin themselves, a participant suggested this solution — fishers should bring the body to scientists on shore for examination. If it is determined that the dolphin did not die by the fisher’s hand, the fisher should be able to keep or be adequately compensated for it. If it is determined that the dolphin was killed by the fisher, then seizure of the carcass and appropriate penalties can be imposed.

In an unrelated event, a fisher in another community several miles away collaborated with scientists to navigate the delicate terrain between cultural practices, livelihood priorities, and scientific investigations, when a dolphin beached on their shore. He deterred local youth from cutting up the carcass to sell, arranged for burial of the body, and enabled scientists to take a sample of the carcass for testing.

1. Did data play a role in stakeholders’ decision-making on this issue?

2. Were stakeholders inclusive in their decision-making? If not, how might they have collaborated to use data to make mutually acceptable decisions?

3. What data collection and decision options could stakeholders map out in anticipation of future beaching of marine mammals or for managing dolphin/human relations?

4. What range of community responses might result from implementation of different decision options?

This case study is based on a research collaboration between SSRD and the Ocean Nexus “Inclusive Data for Decision-Making” project. Co-written with Edwin Boachie-Yiadom, Theophilus Boachie-Yiadom and Kwesi Johnson.

Worksheet 10: Decide how you will make and implement the decision

Purpose: Outline the process and criteria you will use to make and implement a decision based on the data collected

1. Describe how you will use the analyzed data to make decisions about the problem.

2. Describe how you will implement the selected decision(s).

3. Consider these questions:

a. How will each decision option affect different groups?

b. How might different groups react to:

i. the decision itself?

ii. the way the decision is implemented?

c. How could this affect the effectiveness of implementation?

d. Have affected groups agreed to the possibility of decision options that would affect them negatively? Are they still on board now that the option has become a reality?

5. Reflect on participation and representation:

a. Which societal groups will participate, and which groups will not participate in 1) deciding how the data will be used and how decisions will be implemented 2) making and implementing the actual decision? Why? What can be done to broaden participation?

b. What points of view or agendas are likely to be prioritized in the use of the data in decision-making and in implementation? Whose realities, interests and needs are reflected and whose are not? What can be done to broaden representation or mitigate lack of representation?

The chart below illustrates one way to build out possible scenarios.

Sample Process

DECIDING HOW THE IMPLEMENTATION AND IMPACT OF DECISIONS WILL BE EVALUATED

Sailing into the future, guided by the past, represents the way in which we negotiate our traditional and contemporary realities. We sail into the future using the resources that will best suit our intentions. (Nālani Wilson-Hokowhitu)1

From an Indigenous perspective, for evaluation to be true and useful — that is, a good evaluation — the evaluator must have an understanding of the self-determination that fuels the goals and aspirations of Indian communities to preserve, restore, and protect their cultures and ways of doing things. (Joan LaFrance and Richard Nichols)2

PURPOSE OF THIS SECTION

This section outlines some steps for planning to inclusively evaluate the policy or strategies you decide to implement. Planning the evaluation process in advance sets the foundation for reflecting on what did or did not go as expected and how you will respond in different scenarios.

Results:

1. A description of how you will evaluate the effectiveness of your decision

2. Assessment of how inclusive the designed evaluation is

WHAT IS EVALUATION AND WHY IS IT IMPORTANT?

At its most basic level, an evaluation is a reflection on your past actions and their impacts. Once the action you decided upon is underway, evaluation helps to answer questions such as: Did we meet our goals? Why or why not? What should we continue to do in the future? What should we adjust and how? What have we learned about our community and environment from the implementation of our action?

OHENE ANIWA | king’s eye symbol of vigilance

Source: https://commons.wikimedia.org/wiki/File:Ohene_aniwa.svg

1 Nālani Wilson-Hokowhitu. (2019, p.126). The past before us: mo'okū'auhau as methodology. University of Hawai'i Press.

2 Joan LaFrance & Richard Nichols. (2008, p.13). Reframing evaluation: Defining an indigenous evaluation framework. Canadian Journal of Program Evaluation, 23(2), pp.13-31.

Of critical importance, evaluation is also an opportunity to assess the inclusiveness of decision-making plans and actions. Building in processes to assess representation can bring to light any groups that were overlooked or insufficiently included in the decision-making process and show where inclusiveness needs to be strengthened. Properly designed, an evaluation would account for the diverse range of experiences and backgrounds within your community by asking questions such as:

• Has the decision resulted in different outcomes for different communities or groups?

• Did those groups participate in making or implementing the decision?

• Do different groups have different interpretations of the evaluation findings?

• Are different groups more likely to approve (or disapprove) of recommendations emerging from the evaluation?

BEING INCLUSIVE IN EVALUATION PROCESSES

“What looks similar on the surface often turns out to have different meanings and different aims” (Julie Cruikshank)3

The inclusiveness-oriented values and perspectives that guide your decision-making processes should also be present in your evaluation processes. For example, any commitments and agreements you made with your community during the decision-making design should carry over into the evaluation plan. Just as you would have included as many relevant stakeholders as possible in defining the issue, choosing methods, collecting data, analyzing results and making a decision, you should aim to include as many community representatives as possible in deciding how the decision will be evaluated. Wide participation should be sought at each stage for agreements on what will be evaluated, what criteria will be used to determine success or failure, whose views will be sought on the impacts of the decision, and so on.

ASPECTS TO CONSIDER

What is the purpose of the evaluation? Evaluation exercises often have a negative connotation because they are seen as primarily focused on identifying problems and failures. While evaluations might seek areas for improvement, a good evaluation equally aims to understand what is working well and why. If we understand what we are doing well, we can apply that knowledge to what is not as effective and make changes to increase success.

When will evaluation be done? Evaluations can be done during (formative evaluation) and after (summative evaluation) implementation of a decision. Formative evaluations occur at interim points, such as halfway through an implementation period. Such interim assessments provide an opportunity to check whether you are on the right track, spot problems, and make adjustments if things are not going as planned or unintended outcomes are beginning to emerge. Continuous evaluation helps to improve processes, adapt to changes in the community, and increase accountability. Summative evaluations are done at the end of the implementation or project period and are intended to assess the overall impacts of the decision, and especially whether it achieved its goals. Depending on the type of decision, one can also think of evaluation as an ongoing and cyclical activity, with continuous reflecting, learning, and application of lessons to revise actions.

Who will do the evaluation? Although some aspects of evaluation can be undertaken by machines or technology, at the core level, evaluations are led and conducted by people. In western contexts, it is often preferred that an independent person, often from outside the organization or community – an external evaluator –does the evaluation. This trend has led to a perception that evaluation must be done by outsiders. Evaluation is generally most effective when evaluators are openminded and able to act neutrally and without political or social pressure. However, evaluation does not necessarily have to be done by an external person. The processes being evaluated are embedded in community contexts and as such, evaluation methods should reflect the values and experiences that the community deems important. Evaluators must be aware of and sensitive to the cultures and contexts of the community. Sometimes, this might not be possible for someone outside the community. Therefore, inclusiveness in evaluation planning is critical.

What will be evaluated? Oftentimes, evaluators use a theory of change (sometimes called a logic model) to provide structure for assessing the effectiveness of a decision. A theory of change (ToC) outlines the logic behind the decision; that is, how the decision is expected to lead to the desired result. A western logic model often has the following components:

• Inputs. These are the resources (people, money, equipment, supplies, etc.) that will be needed. An inclusive evaluation should consider if there were the appropriate amounts of inputs and if the inputs were the correct ones for the community context.

3 Julie Cruikshank. (2014, p.259). Do Glaciers Listen? Local Knowledge, Colonial Encounters, and Social Imagination. UBC Press.

• Activities. These are the actions that people will undertake. They could involve such varied activities as educational outreach, implementing fishing regulations, or limiting agricultural runoff.

• Outputs. Outputs are the immediate results of the activities. For example, the output of educational outreach on marine conservation could be “500 participants trained in a community workshop.” The fact that 500 people were trained does not by itself improve the marine ecosystem, but it theoretically represents the magnitude of human resource that has acquired the necessary knowledge and skills to improve the marine ecosystem.

• Outcomes. These are short-term changes (often in human behavior) that are expected to follow the outputs achieved in the previous step and lead to the larger desired environmental changes. For example, a desired outcome from training of 500 individuals could be that the majority of participants enjoyed the training or plan to use what they learned in the future.

• Impacts. Impacts are the environmental changes the decision expects to achieve. They can be short, medium, or long-term. For example, reaching 500 individuals through educational outreach could lead to 10% reduction in harvesting of an important fish (short- term), which could then lead to a 20% increase in the fish population (medium/long-term).

• Assumptions. It is important to map out any assumptions you make about things like your community, policies, or the environment. For instance, the decision to train 500 people assumes that people are damaging the marine ecosystem because they lack certain knowledge and that providing them with knowledge will help change their behavior. This assumption might turn out to be wrong but is important to state. Documenting assumptions is important not only because they explain the choice of activities, but also because during the evaluation, they can help explain why an activity did not produce the expected outcome or impact.

This linear approach and components of a ToC may not be appropriate for your community. What would an appropriate theory of change for your community look like?

Sometimes, flaws in a ToC will not be obvious until implementation of a decision begins — being inclusive in the early stages of a decision-making plan can help to minimize this. Likewise, being inclusive at the evaluation stage can help to unearth invisible flaws in the ToC. The more representatives are involved in designing the evaluation, the better the chances of getting to know if the proposed inputs and activities, for example, have inadvertently disadvantaged or sidelined certain populations. Outcomes and impacts can be experienced differently across different communities; a diverse evaluation team helps ensure that environmental impacts are equitably measured. Being inclusive in evaluation design can also help to correct inaccurate assumptions.

What criteria will be used? An evaluation would examine each of the above components to determine whether they produced the targeted results and why. Key to this will be decisions on what constitutes success or failure. A lot of evaluations measure success in terms of outputs, because these are easier to quantify than outcomes and impacts. However, this misses the point if achieving the output does not lead to the targeted impacts. Conversely, blind focus on original intended impacts can lead to discounting or neglect of other unintended impacts that could be positive or negative. Different groups in a community should have a say in defining success criteria, since they might have varying experiences of a decision.

EVALUATION DESIGN

As with research, evaluation methods tend to heavily reflect western approaches. However, there are increasing numbers of indigenous and non-western evaluation frameworks. Many of these new approaches promote inclusive evaluation design and implementation. For example, the American Indian Higher Education Consortium has created a framework based on the belief that “evaluation should also respond to tribal concerns for usefulness, restoration, preservation, and sovereignty, and to do so, it must be grounded in Indigenous epistemologies, responsive to cultural values, and embraced by the communities that it is intended to serve.” 4

Evaluation design involves the same research methods used in decision-making processes. The main difference is that while research in a decision-making process aims to understand an issue of importance in the community and decide on what the community, and others should do. With research in an evaluation, your goal is to understand how the resulting decision, strategy or policy is working.

4 Joan LaFrance & Richard Nichols. (2008). Reframing evaluation: Defining an indigenous evaluation framework. Canadian Journal of Program Evaluation, 23(2), 13.

The types of questions that could be answered in an evaluation include:

• Are the resources allocated to implement the decision adequate?

• Is the decision having its intended impacts?

• Are political, economic, social, or cultural dynamics impacting the effectiveness of the decision?

Your evaluation plan can be divided into four broad phases — design, collect, analyze, act (Figure 11). As with the decision-making process, you can adapt this framework as needed to best represent your community’s practices. be appropriate for your community. What would an appropriate theory of change for your community look like?

1. Design

a. Goals, objectives, and questions. Since you will not be able to evaluate everything, clearly defined evaluation objectives will help to scope a manageable evaluation. Through inclusive planning, you can decide on the most important areas to assess, paying attention not only to the desired impact of the decision, but also the processes, intermediate outcomes, assumptions, and methods for inclusion.

b. Data. What are you trying to achieve, impact, or change with the decision you made? Decide how you will know if that goal has or is being achieved — this will be the evaluation data you want to collect. Are you trying to increase fish populations? What can you measure to track those changes and link them to your decision?

c. Methods. Determine the most appropriate and relevant data collection methods for the evaluation plan. This includes how and when you will collect data and who will participate in the process.

2. Collect. Collect data to aid in reflecting on implementation of the decision.

3. Analysis. Assess and interpret the evaluation data in context of the realities of your community. See what you can learn about how to refine the original decision, what assumptions might need updating, or what shifts in priorities and/or resources might be needed.

4. Act. This crucial last step is to use the evaluation findings to make adjustments to the decision. There is significant opportunity here for increasing inclusiveness and representation through refinement of activities. Action can also involve updating your community and other stakeholders about progress with the plan, using the appropriate languages and formats.

Figure 11: Evaluation Phases

CASE STUDY: INCLUDING INDIGENOUS WORLDVIEWS IN MONITORING ENVIRONMENTAL CHANGE IN FINLAND

The Sa ́mi are the only Indigenous peoples in northern Europe, and like other indigenous groups around the world, they face human-induced environmental change, limited political recognition and autonomy, and the threat of natural resource exploitation. For the Skolt Sa ́mi, Atlantic salmon are a crucial resource. These salmon are managed in the Na¨a¨ta¨mo¨ watershed in Sub-Arctic Finland. Management of Atlantic salmon by the Skolt Sa ́mi has historically been built around their close connection with the environment and complex knowledge of the past, which views environmental change as cyclical “events.” Similar to other arctic communities, they approach timespace in significantly different ways from “linear scientific environmental management models.” Sa ́mi evaluation methods include senses such as taste, smell, feelings, visible and invisible presences, and attachments to a river.

Management of the Na¨a¨ta¨mo¨ watershed, including fishing rights for the Sa ́mi, is primarily controlled by Norway and Finland through the greater Atlantic salmon management structure. Although there have been efforts to include Sa ́mi practices in management, there is a sense by the Sa ́mi peoples that policies governing natural resources did not meaningfully represent the Sa ́mi cyclical and nonlinear views of the world. The result is a feeling of harm to their ecosystems and threats to their ways of life.

To address this, the Skolt Sa ́mi participated in a multigroup initiative that brought together the Sa ́mi, local organizations, and western-trained scientists to experiment with co-constructing science and Indigenous knowledge about environmental change. The participating scientists were selected based upon their openness to Sa ́mi evaluation methods.

The Sa ́mi determined that they first needed a baseline understanding of the current environment in order to evaluate any changes that might result from new policies. As such, an early activity involved the Sa ́mi and scientists jointly documenting the existing weather and water conditions. This included developing an atlas of pre-colonial indigenous water governance practices; and interviewing Sa ́mi fisherfolk in their native language about salmon, place names and historical changes.

Data on the current environment was then captured by recording observations with digital cameras, which were shared with the scientists as visual histories. Data known to be connected to salmon movement, such as water level, water quality, algal blooms and foam, and water temperature, was co-constructed using local monitoring data as well as other publicly available data sources. Analysis of the collected data led to new scientific discoveries, increased interest in joint salmon management systems, decisions to adapt traditional harvesting practices, realization that addressing climate change must include the restoration of “lost” and damaged habitats, and a decision to continue local data collection and knowledge use to inform policy. As a result, amongst other things, a community-based traditional knowledge archive was created to serve the community and future research.

1. In what ways was inclusion practiced in this case?

2. What possible results could have occurred without inclusion?

3. How might the final decisions made about salmon management be evaluated?

Adapted from: T. Mustonen, & P. Feodoroff. (2018). Skolt Sa ́mi and Atlantic Salmon collaborative management of Näätämö watershed, Finland as a case of Indigenous evaluation and knowledge in the Eurasian arctic. In F. Cram, K. A. Tibbetts, & J. LaFrance (Eds.), Indigenous Evaluation. New Directions for Evaluation, 159, 107–119.

Worksheet 11: Decide how you will evaluate your decision-making plan

Purpose: Outline the design criteria and methods you will use to evaluate your plan and its outcomes

1. Describe how you could evaluate the outcomes of

a. the decision-making plan and

b. the effectiveness of decision(s) implemented.

2. Discuss and/or map out the areas to evaluate and describe how you will measure them and collect needed data.

3. Discuss the implications of your processes and evaluation plan. For example,

a. In what ways can different positions of power impact evaluation processes in your community?

b. How can you capture the priorities of different groups when deciding what issues to evaluate?

c. What are your approaches to inclusion when determining:

i. the issues to evaluate and the data needed and

ii. the methods used to collect and analyze evaluation data?

d. Are the approaches different? Why or why not?

e. How might your evaluation plan impact different groups?

4. Reflect on participation and representation.

a. Which societal groups will participate in:

i. deciding on the evaluation methods and

ii. the actual evaluation activities; and which will not?

b. Why? What can be done to broaden participation?

c. What points of view or agendas are prioritized in how decisions are to be evaluated?

d. Whose realities, interests and needs are reflected and whose are not?

e. What can be done to broaden representation or mitigate lack of representation?

f. What are the implications of non-participation or non-representation?

CONCLUSION AND REVIEW 12

(Jennifer Gabrys quoted in Jocelyn Longdon)1

… replace extractive data practices with community-based participatory research and community science projects… in which participants are empowered as fellow researchers and take part in designing the study, collecting data, and analyzing it with an emphasis on co-learning. (Lourdes Vera and others, 2019, p.1015)2

PURPOSE OF THIS SECTION

This section summarizes the key features of inclusive data for decision-making. It provides the opportunity to re-examine all the elements of the D4D plan you have developed and consider its implications for different stakeholder groups in your community.

Results:

1. Assessment of how inclusive your D4D plan is

2. A plan for how to mitigate the impacts of non-participation and non-representation

INCLUSIVE D4D IS A PROCESS AND A GOAL

In addition to being an end goal, inclusive D4D is an aspiration to be continually pursued. Due to the heterogeneity of society and intersectionality of identities, one can never claim to have definitively achieved inclusion. Even inclusive D4D will result in outputs and outcomes with differing degrees of inclusivity. It is the responsibility of stakeholders to set thresholds for what levels of inclusiveness are acceptable for their particular situation,

BI NKA BI | no one should bite the other symbol of peace and harmony

Source: http://www.adinkra.org/htmls/adinkra/bink.htm

1 Jocelyn Longdon. (2020, p.511). Environmental data justice. The Lancet Planetary Health, 4(11), pp.510-511.

2 Lourdes Vera and others. (2019, p.1014). When data justice and environmental justice meet: Formulating a response to extractive logic through environmental data justice, Information, Communication & Society, 22(7), pp.1012-1028, https://doi.org/10.1080/1369118X.2019.1596293

‘having a plurality of systematic approaches and ontologies is really important in capturing other experiences’, but one of the major issues in the field of data science, is there commonly being only ‘one accepted way of collecting data, monitoring problems, and designing studies.’

with the overall aim to maximize participatory knowledgemaking and contextualization of data. Collecting and using data is unavoidably extractive in nature; as such, the search for ways to balance out the exploitative and exclusionary tendencies (e.g., by improving inclusive processes) should never end. Before moving to the implementation of a D4D plan, take a step back to review the entire plan to identify blind spots and potential unintended exclusions, confirm the choices made within existing constraints, acknowledge inclusions and exclusions, and hold yourself accountable to constantly seek higher levels of inclusion.

Source: http://www.adinkra.org/htmls/adinkra/fiha.htm

INCLUSIVE D4D REVIEW

1. Developing a code of conduct

Goal — to establish ground rules for engagement that enable a safe, respectful, and accepting environment for all participants.

Collaboratively setting these guidelines will hopefully make it easier for all stakeholders to feel comfortable sharing their views, especially where there are significant internal and external power differentials.

> Assess who participates in developing the code of conduct and whether the code creates a safe and inclusive environment.

2. Defining community

Goal — to identify the entities that are affected by the issues under consideration.

Deliberately outlining the community as it applies to your agenda forces consideration of the parameters of that community, what elements are covered or left out of your D4D planning, whether some elements are or should be prioritized over others, and whether your code of conduct addresses the circumstances of all community members.

> Assess who participates in defining the community and whether the definition excludes any relevant stakeholders.

3. Identifying decision-making cultures

Goal — to agree on what values and behaviors will guide decision-making in the context of your D4D plan.

Identifying the typical decision-making culture or cultures shows the power structures in which stakeholders are embedded and provides an opportunity to consciously seek a more even distribution of power for the decision at hand.

> Assess who participates in identifying decision-making cultures and whether the approach you decided on reflects the dominant culture or enables more equitable sharing of decision-making power.

4. Fostering inclusion in the decision-making process

Goal — to ensure that as many social groups as possible participate and/or are represented as equal partners in the decision-making process.

While it might not be possible to include every group or point of view, aspiring to do so will heighten sensitivity to marginalized groups and, ideally, broaden the net of who and what does get included. Awareness of excluded groups should also encourage reflection on how their exclusion can be mitigated.

> Assess who participates in determining how to foster inclusion, and what community voices are included and excluded from your decision-making process.

5. Ways of knowing and inclusive D4D guiding principles

Goal — to agree on what principles and approaches to knowledge will guide the research design.

If done with an open mind, articulating existing ways of knowledge for different groups and coming to an agreement on how these can be applied to the research design will facilitate sharing of different approaches to knowledge. In the process, ask questions about whose approaches tend to take precedence over others.

> Assess who participates in determining your research guiding principles and whether your decision-making plan can accommodate different ways of knowing and approaches to knowledge.

FIHANKRA | house/compound symbol of security and safety

6. Defining the issue

Goal — to select and describe the issue that will be addressed in your decision-making plan.

Genuinely committing to inclusion when defining the issue enables you to set aside personal agendas and allow other agendas the opportunity to be considered and negotiated before a choice is made.

> Assess who participates in defining the issue, whose interests are represented in the selected issue, and whose interests are given lower priority.

7. Identifying data needs

Goal — to determine what kinds of information you will collect.

Reflecting on the degree of inclusion when determining data needs provides another opportunity to consider how different ways of knowing influence data-based decision-making. It should also prompt attention to whose information needs are being prioritized.

> Assess who participates in identifying data needs, whose interests are represented in the selected data or indicators, and whose interests are given lower priority.

8. Choosing data collection methods

Goal — to select the methods you will use to collect the data.

Choosing data collection methods with inclusivity in mind increases sensitivity to how research processes can willfully or inadvertently sideline potentially important people and groups.

> Assess who participates in data collection decisions and whether the methods chosen limit who can participate as data collectors or as respondents.

9. Deciding on data analysis approach

Goal — to decide how you will analyze the data you collect.

Inclusiveness in data analysis is challenging but can generate unexpected insights and guard against blind spots when interpreting results.

> Assess who participates in the data analysis component and whether the approach chosen could privilege or lead to bias against particular groups.

10. Deciding how decisions will be made (and implemented)

Goal — to decide how you will use the research findings to decide on a course of action.

Decision-making can potentially be improved by proactively including affected groups in the selection of the criteria by which decisions will be made, as well as in the choice of implementation strategies.

> Assess how you will make a decision using the results of your data analysis, and whether the approach chosen addresses diverse needs and is appropriate for the different stakeholders involved or affected.

11. Deciding how the implementation and impact of decisions will be evaluated

Goal — to make a plan for how you will evaluate the implementation of your decision and what outcomes or impact it had.

An evaluation plan is essential to map out expected outcomes and how they will be identified. Including diverse parties in developing the plan is an opportunity to incorporate different notions of success and failure.

> Assess who participated in deciding on evaluation methods, and the extent to which the methods chosen reflect the perspectives and interests of different groups.

Based on the above, assess the extent to which your decision-making plan is inclusive in all its components. Note whether some components are more inclusive than others and revisit the rationale for those choices to see if the less-inclusive elements can be made more inclusive, either through participation, representation, or some other means.

Scenes of life in ancient Hawai'i. Image credit: Solomon Robert Nui Enos, https://www.instagram.com/solomonenos/

CASE STUDY:

PARTICIPATORY MONITORING OF FISHERIES IN KIRIBATI AND VANUATU

This case study was co-written with members of the Pathways project team. The following project members were instrumental in the design of the participatory monitoring described below: Neil Andrew, Brooke Campbell, Aurélie Delisle, Owen Li, Pita Neihapi, Beia Nikiari, Abel Sami, Dirk Steenbergen, and Tarateiti Uriam. Guidance from the Pacific Community (SPC-FAME), national fisheries agencies, and other regional practitioners is also gratefully acknowledged.

Designing and implementing truly inclusive and fit-forpurpose participatory research processes is challenging. Building on long-term engagements with people and place, the Pathways project* – a collaboration between the University of Wollongong, national fisheries agencies in Kiribati, Solomon Islands, and Vanuatu, the Pacific Community (SPC-FAME), and WorldFish – used participatory research approaches to co-develop an evaluation process for the performance of community fisheries management plans. These plans were codesigned and implemented with participating communities during earlier phases of the project. The request for evaluation came to project partners from the fishers within these communities, who were keen to see evidence of the material impacts that fisheries management measures were having on community marine fisheries resources. As the situations below illustrate, even with a project’s strong existing relationships and careful consideration of local contexts and sensibilities, the co-development of a fishery monitoring program to support adaptive community fisheries management can still generate opportunities for learning and improving the ways that inclusive participation manifests in practice.

Adapting data collection methods to the community context. Based on past experiences, the project team developed fisheries data collection and reporting processes that prioritized: relationship-building, collaborative development of monitoring activities, and ensuring that results are directly useful to the community, instead of focusing on maintaining statistically rigorous data collection (as defined by Western research standards). This approach required “a series of choices and compromises that balance practicalities and ambitions for information” to create a monitoring program “that was legitimate, simple, practical and useful for our purpose” (Andrew et al., 2020, p.33). The team had to balance opportunity costs regarding a range of issues including “cost, simplicity, appropriateness, feasibility, scalability, legitimacy and adaptability” (Andrew et al., 2020, p.33) all of which had implications for what data could or could not be collected. A choice was made not to use commonly applied creel survey methods of individually identifying, measuring, and weighing fish and invertebrates at the landing site, which often burdens fishers with lengthy and involved data collection procedures in the field. Instead, an approach was applied that digitally photographed entire catches so that species identification, length, and weight estimates could be determined back in the office using known measurement ratios. This approach had several advantages for a small-scale fisheries and community-based program context, including being more time and effort efficient for fishers, avoiding expensive and unreliable weighing processes at the landing site, and enabling greater accuracy and consistency in identifying species. Notably, the project found that their approach did “not require the [physical] presence of international fish and invertebrate taxonomy experts” (Sami et al., 2020, p. 44). However, the approach requires good photography and curation skills as well as access to taxonomy expertise.

Examples of photographed catches. Photo credits: Pathways project.

Adapting methods in these ways is helping to engender sustained engagement between communities and national fisheries management agencies, to demonstrate the impacts of particular interventions, and to reduce the burden of fishery monitoring on communities and fishers.

Being sensitive to excluded groups. Adequately capturing women’s fishing activities was challenging in some communities. In some cases, women voiced that they did not consider their coastal gleaning activities to be noteworthy compared to the reef and ocean fishing done by men. The project engaged extensively with various community groups to ensure that women’s catch contributions were appreciated by all, including women themselves; and thus seen as equally important for fisheries management planning and decision-making in communities. Being approached by male catch monitors also contributed to some women’s apprehension to participate. Reflecting on this issue, fishery monitoring teams adapted to include women catch monitors in future data collection rounds.

Being sensitive to power dynamics. In consultation with community leaders, one community’s official fisheries officer was selected to be a member of the data collection team. However, due to this person’s formal duties as an enforcer of national regulations, fishers in the community were reluctant to reveal their catch. The fishery monitoring team realized that a catch monitor’s pre-existing position or role in the community cannot be isolated from their role as a catch monitor. Following community consultation, a new catch monitor was chosen without this ‘conflict of interest.’

Being sensitive to knowledge traditions. Respecting local norms, traditions, and beliefs can present a challenge to achieving program goals. For example, in Kiribati, the Pathways team found that some fishers were concerned about sharing information about their fishing grounds (atiibu or kabwate), which are closely held family secrets (Nikiari et al., 2020). This was resolved by discussing with fishers the reasons for their discomfort and reaching an agreement to collect more generalized data that identified habitat types where harvesting was occurring rather than specific locations.

Engaging fishers as active participants in the fisheries management ‘cycle.’ Data collection and analysis is only one part of effective monitoring and evaluation of the performance of community fisheries management plans. Communicating the results of collected fishery monitoring data back to communities and governments in a way that is both understandable and useful for adaptive management decision-making is also important but can take considerable time and effort to do (Gereva et al., 2021). The project team developed reporting templates for both government and communities containing main findings to present and discuss in agency and communitywide meetings. These were presented after the analysis of the first round of data. Feedback was actively sought around what was good about the data presented and what was missing to help with understanding and management decision-making. This led to gradually refined presentations and opened the opportunity for positive and open discussions about fishing impacts and desired management objectives.

Building local capacities to undertake independent research. Aware that fishery monitoring programs are often difficult to sustain once external technical supports are removed, the project team made significant efforts to build up local capacities to design and deliver research, both for country-based project staff and for partner fishery agency staff. Efforts included: multiple training events for incountry and fishery agency staff in basic research design, data collection, data analysis, and data management skills; providing training skills and resources to run training independently in the future; and providing higher education scholarship opportunities for I-Kiribati, Ni-Vanuatu, and Solomon Island nationals in related research areas.

Discussion Questions:

1. What do you think about the research design choices made by the Pathways project?

2. Have you faced similar gender, power, or cultural challenges in your work? How did you handle them?

3. If you had to assess how inclusive this project’s research processes were, what additional information would you want to have?

*The ‘Pathways’ project is the shorthand name for a series of consecutive Australian government-funded initiatives into strengthening community-based fisheries management in the Pacific (through ACIAR projects FIS-2012-074, FIS-2016-300, and FIS-2020-172). For the publicly available final project report, see ‘Sources’ below. Sources: Neil Andrew and others. (2017). Improving community-based fisheries management in Pacific Island countries. Canberra, Australia, Australian Centre for International Agriculture Research. Neil Andrew and others. (2020). Developing participatory monitoring of community fisheries in Kiribati and Vanuatu. SPC Fisheries Newsletter, 162, 32–38. Sompert Gereva and others. (2021). Reflecting on four years of community-based fisheries management development in Vanuatu. SPC Fisheries Newsletter, 165, 55-67. Beia Nikiari and others. (2020). Piloting a community-driven catch monitoring approach in Kiribati. SPC Fisheries Newsletter, 163, 34–39. Abel Sami and others. (2020). A novel participatory catch monitoring approach: The Vanuatu experience. SPC Fisheries Newsletter, 162, 39–45. University of Wollongong (2021). Technical and Training Manual for Catch Monitors, Modules A&B. Australian National Centre for Ocean Resources and Security, University of Wollongong, Australia. https://purl.org/spc/digilib/doc/chkpw and https://purl.org/spc/digilib/doc/4pfz6

ACTIVITY: REVIEW THE D4D PLAN

In this activity you will assess the extent to which your final D4D plan is aligned with the values of different stakeholder groups, how inclusive its components are, and why. Reflect on whether the components and totality of the plan have a greater or smaller likelihood of promoting just outcomes for the community.

• Review your entire D4D plan in Worksheets 12a, 12b and 12c.

Worksheet 12: Review your D4D plan

Purpose: To assess the appropriateness and inclusiveness of your D4D plan as a whole. Use the reflection questions below and sample worksheets to review the plan.

For each component of your D4D plan reflect on and discuss the following questions:

• Participation

• Which societal groups will participate in deciding how the component is designed and which groups will not?

• Which societal groups will participate in implementing that component and which groups will not?

• What are the reasons for inclusion and exclusion?

• Have these reasons been mutually agreed upon?

• Did decisions on inclusion and exclusion happen through a process of negotiation or were they made by a section of the community?

• Representation

• Which societal groups have their points of view or agendas represented in the plan?

• Overall, whose realities, interests, worldviews, and needs are prioritized and whose are not?

• Is there a possibility that the decisions were affected by power dynamics within the community?

• Did you encounter any community norms or values that were challenging to incorporate?

• Are some components of the plan more inclusive than others?

• Is it possible to further broaden participation and representation in designing and implementing the plan?

• If yes, how?

• If not, can anything be done to address the implications of non-participation or non-representation?

• Content of the plan

• Will the implementation of each component enable the community to acquire data that it wants, trusts, and owns?

• Could the implementation of each component result in harm or discomfort for groups in the community?

• Will implementation of the plan lead to decisions that the community will be willing to abide by?

• Based on your reflections and discussions, does the plan have a greater or smaller likelihood of promoting just outcomes for members of the community?

Worksheet 12a: Identify who is participating and/or represented in the decision-making plan

For each of the decision-making plan components, list:

• Groups that will or have participated

• Groups that will not or did not participate

• Groups whose interests and worldviews are represented

• Groups whose interests and worldviews are prioritized

• Groups whose interests and worldviews are not represented or prioritized

D4D plan components (Add or replace components as appropriate)

Developing a code of conduct

Defining community

Identifying decision-making cultures

Fostering inclusion

Ways of knowing and inclusive D4D guiding principles

Defining the issue

Identifying data needs

Choosing data collection methods

Deciding on data analysis approach

Deciding how decision will be made

Deciding how implementation and impact of decisions will be evaluated

Sample Worksheet

Worksheet 12b: Assess the inclusiveness of the decision-making plan

For each D4D plan component, use your preferred metric (e.g., high, medium, low) to categorize how inclusive the plan for that component is. Based on the results for individual components, assess how inclusive the overall plan is. Discuss reasons for the inclusiveness level of individual components and whether action is needed to increase inclusivity or mitigate exclusion.

Sample Worksheet

D4D PLAN COMPONENTS

(Add or replace components as appropriate)

Defining community

Identifying decision-making cultures

Fostering inclusion in the decision-making process

Ways of knowing and inclusive D4D guiding principles

Defining the issue

Identifying data needs

Choosing data collection methods

Deciding on data analysis approach

Deciding how decision will be made (and implemented)

Deciding how the implementation and impact of decisions will be evaluated

OVERALL

INCLUSIVENESS SCORE

(Explain the reason for this score)

Worksheet 12c: Describe what you will do to mitigate the impacts of non-participation and non-representation of different groups

Identify excluded groups and describe what you will do to mitigate the impacts of non-participation and non-representation of different groups. You can use the Marginalized Voices Framework (see section 4) or any other approach to categorize different groups in your community.

Sample Worksheet GROUPS (List relevant groups)

Image credit: Solomon Robert Nui Enos, www.instagram.com/solomonenos/