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Record W2790434735 · doi:10.1093/ahr/123.1.241

Thomas C. Leonard. Illiberal Reformers: Race, Eugenics, and American Economics in the Progressive Era.

2018· article· en· W2790434735 on OpenAlexaff
Randall Hansen

Bibliographic record

VenueThe American Historical Review · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEugenicsOpposition (politics)SupporterRace (biology)PoliticsLawBirth controlPolitical scienceSociologyGender studiesHistoryGenealogyPopulationFamily planningDemography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.554
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.256
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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