Racialization and Reproduction: Asian Immigrants and California’s Twentieth-Century Eugenic Sterilization Program
Bibliographic record
Abstract
Abstract During the twentieth century, state health authorities in California recommended sterilization for over 20,000 individuals held in state institutions. Asian immigrants occupied a marginalized position in racial, gender, and class hierarchies in California at the height of its eugenic sterilization program. Scholars have documented the disproportionate sterilization of other racialized groups, but little research exists connecting the racist, gendered implementation of Asian immigration restriction to the racism and sexism inherent in eugenics. This study examines patterns of coercive sterilization in Asian immigrants in California, hypothesizing higher institutionalization and sterilization rates among Asian-born compared with other foreign- and US-born individuals. We used complete count census microdata from 1910 to 1940 and digitized sterilization recommendation forms from 1920 to 1945 to model relative institutionalization and sterilization rates of Asian-born, other foreign-born, and US-born populations, stratified by gender. Other foreign-born men and women had the highest institutionalization rates in all four census years. Sterilization rates were higher for Asian-born women compared with US-born [Incidence Rate Ratio (IRR) = 2.00 (95% CI: 1.61, 2.48)] and other foreign-born women (p < 0.001) across the entire study period. Sterilization rates for Asian-born men were not significantly higher than those of US-born men [IRR 0.95 (95% CI 0.83, 1.10). However, an inflection point model incorporating the year of sterilization found higher sterilization rates for Asian-born men than for US-born men prior to 1933 [IRR 1.31 (95% CI 1.09, 1.59)]. This original quantitative analysis contributes to the literature demonstrating the health impact of discrimination on Asian-Americans and the disproportionate sterilization of racial minorities under state eugenics programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".