Masters of Health: Racial Science and Slavery in U.S. Medical Schools
Bibliographic record
Abstract
Masters of Health, by Christopher D. E. Willoughby, is a compelling exploration of how ideas about race were constructed by American medical professionals in the nineteenth century and then used to increase their recognition as experts. Willoughby shows how these race ideas developed among intellectuals and were then spread within the general population, as doctors trained in U.S. medical schools practiced throughout the United States. The growing number of northern and southern medical schools during the nineteenth century included discussions of race as part of their core curricula. Willoughby clearly alludes to both the shifting scientific explanations offered about racial difference and the consistency of white supremacy in these schools' teachings. Examining the intense debate between polygenesis and monogenesis origins, for example, he shows how, despite a clear philosophical divide, both sides ultimately embraced a common belief in the superiority of whites over all other races. Medical educators used a variety of methods to inculcate their own beliefs in those they taught, creating a “clinical racial gaze” (p. 114). Masters of Health looks deeply at the lectures and textbooks assigned to medical students by well-known professors such as Joseph Leidy of the University of Pennsylvania and Oliver Wendell Holmes Sr. of Harvard University. Just as importantly, Willoughby analyzes over four thousand dissertations written by medical students between 1807 and 1861 to demonstrate how these ideas were processed and reiterated by those who encountered them as part of their education.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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".