Racial Equity, Diversity and Inclusion in Bioethics: Recommendations from the Association of Bioethics Program Directors Presidential Task Force
Bibliographic record
Abstract
Recent calls to address racism in bioethics reflect a sense of urgency to mitigate the lethal effects of a lack of action. While the field was catalyzed largely in response to pivotal events deeply rooted in racism and other structures of oppression embedded in research and health care, it has failed to center racial justice in its scholarship, pedagogy, advocacy, and practice, and neglected to integrate anti-racism as a central consideration. Academic bioethics programs play a key role in determining the field's norms and practices, including methodologies, funding priorities, and professional networks that bear on equity, inclusion, and epistemic justice. This article describes recommendations from the Racial Equity, Diversity, and Inclusion (REDI) Task Force commissioned by the Association of Bioethics Program Directors to prioritize and strengthen anti-racist practices in bioethics programmatic endeavors and to evaluate and develop specific goals to advance REDI.
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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 | 0.272 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.029 | 0.020 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.047 | 0.071 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".