Bibliographic record
Abstract
This article explores the political and intellectual context of a controversy arising from a proposal made at the 1959 meetings of the American Society of Blood Banks to divide the blood supply by race. The authors, a group of blood-bankers and surgeons in New York, outlined difficulties in finding compatible blood for transfusion during open-heart surgery, which they attributed to prior sensitization of their patient, a Caucasian, by a previous transfusion from an African American donor. Examining the statistical distribution of blood-group antigens among the various races, they concluded that risk of adverse hemolytic reactions and the cost of testing could be reduced by establishing separate donor pools. The media reported the suggestion, which, given the political climate of the day, rapidly became a public issue involving geneticists, blood-bankers, physical anthropologists, and the African American medical community. Liberals condemned it, whereas eugenically inclined segregationists used the finding to support their views concerning evolutionary distance between the races and the dangers of miscegenation. Here we examine the contribution of comparative racial serology to this affair, the arguments and background of the main players, and the relevance of the debate to discussions about the role of "race" in post-genomic 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".