Race and ancestry in biomedical research: exploring the challenges
Bibliographic record
Abstract
The use of race in biomedical research has, for decades, been a source of social controversy. However, recent events, such as the adoption of racially targeted pharmaceuticals, have raised the profile of the race issue. In addition, we are entering an era in which genomic research is increasingly focused on the nature and extent of human genetic variation, often examined by population, which leads to heightened potential for misunderstandings or misuse of terms concerning genetic variation and race. Here, we draw together the perspectives of participants in a recent interdisciplinary workshop on ancestry and health in medicine in order to explore the use of race in research issue from the vantage point of a variety of disciplines. We review the nature of the race controversy in the context of biomedical research and highlight several challenges to policy action, including restrictions resulting from commercial or regulatory considerations, the difficulty in presenting precise terminology in the media, and drifting or ambiguous definitions of key terms.
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 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 | 0.241 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.023 | 0.149 |
| Scholarly communication | 0.039 | 0.061 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.028 | 0.035 |
| Insufficient payload (model declined to judge) | 0.005 | 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".