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Insight News
November 15, 2021 - November 21, 2021
Vol. 48 No. 46• The Journal For Community News, Business & The Arts • insightnews.com
Lady J
Shaman & seeker Jacqueline Lady J Maddix
ROOT WISDOM: From the Elders’ Circle Sharing Our Stories
By: W.D. Foster-Graham Book Review Editor By Jacqueline “Lady J” Maddix “What we give is what we receive.” This is one of the many lessons shared by author and KFAI Blues radio personality Jacqueline Maddix, aka Lady J, in her book Roots of Wisdom: From the Elders Circle. As we follow her spiritual journey, we are given the strong sense of traveling between the physical and the metaphysical, a journey that has been given
numerous terms by different teachings through the ages, but it has been a part of us from the beginning. Lady J makes clear the distinction between religious behavior (e.g. what we do on Sunday morning) and spirituality (who we are the rest of the week). Although we as humans like to compartmentalize, she also establishes the correlation and common connections of our teachings and beliefs into one of divine consciousness and unity consciousness. In her book, she uses the term magic to describe the universal energy flow that has always existed, though it is known by many names. Her story begins with self-evolution/self-actualization. As the result of an epiphany in
1993, she took the first step in walking between the physical and the metaphysical worlds, drawing on the ancient wisdom and teachings to resonate in a daily relationship with Spirit and its higher consciousness. Roots of Wisdom reminds us that what we refer to by such terms as New Age has always been here. What shows up in our lives is determined by our level of understanding, for the brain is limited; the mind (consciousness) is unlimited. Lady J guides us through the seven States of Consciousness, from the First State (deep sleep) to the Seventh State (unity consciousness), as well as giving the reader tips for conscious navigation through the stages of consciousness for spiritual
health and positive, constructive choices. As a child, I grew up with the church teaching “you reap what you sow.” Lady J
makes this abundantly clear throughout the book, reminding us that whatever we’re paying attention to is what we are giving our energy to, be it positive or negative, which in turn fuels our intentions and creates the results in our lives. If we give love, we receive love. If we give respect, we receive respect. Hence, the importance of what we think before we act. Tied in with this is “letting go and letting God,” that the Divine Will be done. In the section “The Way of the Warrior,” Lady J discusses how to ground this energy and use it responsibly in positive ways. In this context, she defines the term warrior as “a person who lives in between the worlds of physical and metaphysical existence with
the kind of discipline that is necessary to balance both experiences”; to be in the world but not of it, someone who both walks the walk and talks the talk. A native of St. Paul, Jacqueline “Lady J” Maddix has shared this amazing story of her life and her ongoing spiritual journey as a shaman and a seeker. Her study of international cultures and philosophies confirms the common thread of consciousness that runs through all of them. This is a book of life lessons, growth, and wisdom that we can all aspire to and utilize, wherever our level of understanding is.
Matching tweets to ZIP codes can spotlight hot spots of COVID-19 vaccine hesitancy By Mayank Kejriwal Research Assistant Professor of Industrial & Systems Engineering, University of Southern California Public health officials are focusing on the 30% of the eligible population that remains unvaccinated against COVID-19 as of the end of October 2021, and that requires figuring out where those people are and why they are unvaccinated. People remain unvaccinated for many reasons, including belief in unfounded conspiracy theories about the disease, the vaccines or both; distrust of the medical establishment; concerns about risks and side effects; fear of needles; and difficulty accessing vaccines. To target their messaging and outreach geographically and according to the type of hesitancy, public health officials need good data to guide their efforts. Traditional survey methods are helpful but tend to be expensive. Another approach is to assess vaccine hesitancy through the lens of social media. As an artificial intelligence researcher, I analyze social media data using machine learning. My latest research,
conducted with graduate student Sara Melotte and accepted for publication in the journal PLOS Digital Health, predicts the degree of vaccine hesitancy at the ZIP code level in U.S. metropolitan areas by analyzing geo-located tweets. We found that by processing geo-located Twitter data using readily available machine learning techniques, we could more accurately predict vaccine hesitancy by ZIP code than by using attributes of ZIP codes like average home price and number of health care and social services facilities. The limits of surveys Surveys, such as a Gallup COVID-19 survey launched in 2020, estimate vaccine hesitancy levels in the general population by polling a representative sample with a Yes/No vaccine hesitancy question: If a Food and Drug Administration-approved vaccine to prevent coronavirus/ COVID-19 was available right now at no cost, would you agree to be vaccinated? The estimated vaccine hesitancy is the percentage of individuals who respond “No.” As demonstrated both in our research and work by others, factors such as location, income and education levels all correlate with vaccine hesitancy. A general disadvantage of
such surveys is that detailed questions are expensive to administer. Sample sizes tend to be small due to cost constraints and non-response rates. The latter has been exacerbated recently by political polarization. Computational social science methods, which use computer algorithms to analyze large amounts of data, are another option, but they can have trouble interpreting noisy social media text to glean insights. Mining Twitter Our work takes on the challenge of using publicly available Twitter data to accurately predict vaccine hesitancy in a given ZIP code. We focused on ZIP codes in major metropolitan areas, which are known for high tweeting activity. Users also enable GPS more often in these areas. As a first step, we downloaded all the tweets from a publicly available dataset called GeoCoV19, which filters tweets to be as relevant to COVID-19 as possible. Next, using peer-reviewed methodology, we filtered the tweets down to GPSenabled tweets from the top metropolitan areas. We then randomly split the tweets into a training set and a test set. The former was used to develop
AP Photo/Teresa Crawford
Public health officials need to know where to focus their vaccination outreach efforts. the model, while the latter was used to evaluate the model. Training a model to predict the vaccine hesitancy of a ZIP code is like drawing a straight line through a set of points so that the line comes as close as possible to the center of the points, known as a line of best fit. The line indicates the trend in the data. The first step is converting the raw text of tweets into data points. Recently developed deep neural networks are able to automatically convert the text into data points so that
tweets with similar meanings are closer together. We essentially used such a network to convert our tweets to data points and then trained our machine learning model on those data points. We validated our model using the Gallup COVID-19 survey results. Our method performed better at predicting high levels of vaccine hesitancy than methods that only use generic features, like average home prices within the ZIP code, rather than social media data. We also showed our model
to be effective in the presence of tweets that aren’t related to vaccines or COVID-19. The GeoCov19 dataset is good but includes many tweets that are not relevant specifically to vaccines and a small – but nontrivial – fraction that are not relevant to COVID-19 at all. Early detection and prevention In research currently undergoing peer review, we developed algorithms that
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