Medical Stigmata: Race, Medicine, and the Pursuit of Theological Liberation, by Kirk A. Johnson
Bibliographic record
Abstract
Over the past few years in the United States, issues of race and reconciliation have been at the forefront of societal concerns.In particular, the growth of the Black Lives Matter movement in 2020 unveiled the perpetuation of deeply entrenched racist attitudes and consequentially the systemic nature of racism across the United States.Kirk A. Johnson's recent publication Medical Stigmata: Race, Medicine, and the Pursuit of Theological Liberation tackles this issue directly, focusing on the systemic racism inherent in the practice of medicine.Johnson's work examines the historical maltreatment of Black bodies through the predominance of race-based medicine (RBM) -the use of race as a biological category within the medical field as "the primary indicator for the predispositions of certain diseases" (9) -in the American medical system since the late-nineteenth century, and how Black theology was used as a mechanism of solidarity to combat racial prejudice in medicine.Johnson, an associate professor at Montclair State University, provides a multidisciplinary background on the issue.His knowledge of the Medical Humanities and Religious Studies, as well as serving as a member on the Atlantic Health Systems Bioethics Committee, proves to be particularly valuable in elucidating the connection between medicine, race, and religion.The focus of his work is framed around a case study of the first race-based drug "BiDil" which was initially developed by scientists and later cleared by the Federal Drug Administration (FDA) as a drug specifically designed to treat Black people with heart disease.His case study which examines the path to approval
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".