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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".