Thomas C. Leonard. Illiberal Reformers: Race, Eugenics, and American Economics in the Progressive Era.
Bibliographic record
Abstract
Eugenics—the practice of encouraging the breeding of the “fit” while discouraging the breeding of the “unfit”—has attracted extensive scholarly interest in the last thirty years. As Thomas C. Leonard argues in his fine book Illiberal Reformers, eugenics was not, as many continue to insist on believing, marginal pseudo-science. It was science, and not only its basic precepts (that “feeblemindedness” was hereditary and that feebleminded persons’ higher birth rates would lead to race suicide) but also its policy recommendations (excluding the eugenically unfit via migration control, birth control, and forced sterilization) were widely shared and endorsed by the scientists, university presidents, journal editors, constitutional court justices, and upper-middle-class activists (among others) who defined American public debate. At least one U.S. president, Woodrow Wilson, had once been a strong supporter of eugenics and might well have tempered his support once in the Oval Office only out of political expediency (xii–xiii). Indeed, until the 1920s, critics of eugenics were the marginal ones, and they often founded their opposition to eugenics in nonscientific doctrines such as Roman Catholicism.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".