THE OXFORD HANDBOOK OF THE HISTORY OF EUGENICS
Bibliographic record
Abstract
Contributors -- Abbreviations -- Introduction: eugenics and the modern world / Philippa Levine and Alison Bashford -- Pt. I. Transnational themes in the history of eugenics -- 1. The Darwinian context: evolution and inheritance / Diane B. Paul and James Moore -- 2. Anthropology, colonialism, and eugenics / Philippa Levine -- 3. Race, science, and eugenics in the twentieth century / Marius Turda -- 4. Eugenics and the science of genetics / Nils Roll-Hansen -- 5. Fertility control: eugenics, neo-Malthusianism, and feminism / Susanne Klausen and Alison Bashford -- 6. Disability, psychiatry, and eugenics / Mathew Thomson -- 7. Eugenics and the state: policy-making in comparative perspective / Véronique Mottier -- 8. Internationalism, cosmopolitanism, and eugenics / Alison Bashford -- 9. Gender and sexuality: a global tour and compass / Alexandra Minna Stern -- 10. Eugenics and genocide / A. Dirk Moses and Dan Stone -- Pt. II. National/colonial formations -- 11. Eugenics in Britain: the view from the Metropole / Lucy Bland and Lesley A. Hall -- 12. South Asia's eugenic past / Sarah Hodges -- 13. Eugenics in Australia and New Zealand: laboratories of racial science / Stephen Garton -- 14. Eugenics in China and Hong Kong: nationalism and colonialism, 1890s-1940s / Yuehtsen Juliette Chung -- 15. South Africa: paradoxes in the place of race / Saul Dubow -- 16. Eugenics in colonial Kenya / Chloe Campbell -- 17. Eugenics in postcolonial Southeast Asia / Sunil S. Amrith -- 18. German eugenics and the wider world: beyond the racial state / Paul Weindling -- 19. Eugenics in France and the colonies / Richard S. Fogarty and Michael A. Osborne -- 20. Eugenics in the Netherlands and the Dutch East Indies / Hans Pols -- 21. The Scandinavian states: reformed eugenics applied / Mattias Tydén -- 22. The first-wave eugenic revolution in southern Europe: science sans frontières / Maria Sophia Quine -- 23. Eugenics in Eastern Europe, 1870s-1945 / Maria Bucur -- 24. Eugenics in Russia and the Soviet Union / Nikolai Krementsov -- 25. Eugenics in Japan: sanguinous repair / Jennifer Robertson -- 26. Eugenics in interwar Iran / Cyrus Schayegh -- 27. Eugenics and the Jews / Raphael Falk -- 28. Eugenics policy and practice in Cuba, Puerto Rico, and Mexico / Patience A. Schell -- 29. The path of eugenics in Brazil: dilemmas of miscegenation / Gilberto Hochman, Nísia Trindade Lima, and Marcos Chor Maio -- 30. Eugenics in the United States / Wendy Kline -- 31. Eugenics in Canada: a checkered history, 1850s-1990s / Carolyn Strange and Jennifer A. Stephen -- Epilogue: where did eugenics go? / Alison Bashford -- Chronology -- Index
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.069 | 0.033 |
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".