Recognizing Racism in US Bioethics as the Subject of Bioethical Concern
Bibliographic record
Abstract
Attending to racism and US bioethics raises the question of whether and how racism in bioethics has been the subject of bioethical scrutiny. Bioethics has certainly brought its analytical tools to bear on racist aspects of clinical care and biomedical research. But has bioethics studied racism in bioethics as its subject? A close examination of relevant reports, articles, and books in the US bioethics literature published in the early days of the field, pre-2000, shows mixed findings. In the 1970s, racism as a bioethical concern was variously nonexistent, vaguely implied, and powerfully examined and condemned. In the late 1980s/early to mid-1990s, racism was more frequently described and critiqued, often in the context of discussions about African American perspectives of biomedical ethics and inequities in health care. Understanding how racism in bioethics has been addressed as an ethical concern has consequences for the historical narratives told about the field, for antiracist bioethics work today, and for envisioning an antiracist future for bioethics.
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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 | 0.058 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.024 | 0.088 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".