Racial Science Now: Histories of Race and Science in the Age of Personalized Medicine
Bibliographic record
Abstract
The revitalization of race-based science and medicine at the very monient in which the history of "race" in science gained such widespread critical attention forces difficult questions regarding the success of the field. This article outlines the current debate over race in contemporary biomedical research and offers a case study of the RaceSci: A History of Race in Science Web project. One of the earliest electronic resources devoted to the history of race in science, RaceSci was relaunched in early 2007 to expand its focus on the present. To date, historians are generally absent from the academic and public dialogue on the "return" of racial science. In response, RaceSci aims to better engage historians with the raced-based organization of current scientific research, particularly in genetics, drug development, and the rise of so-called "personalized" medicine.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".