Fixing the Poor: Eugenic Sterilization and Child Welfare in the Twentieth Century by Molly Ladd-Taylor
Bibliographic record
Abstract
Reviewed by: Fixing the Poor: Eugenic Sterilization and Child Welfare in the Twentieth Century by Molly Ladd-Taylor Jon Wlasiuk Ladd-Taylor, Molly – Fixing the Poor: Eugenic Sterilization and Child Welfare in the Twentieth Century. Baltimore: Johns Hopkins University Press, 2017. Pp. 275. The state of Indiana passed the first eugenic sterilization law in 1907, and over the next eighty years thirty-two states sterilized more than 63,000 Americans often without prior consent and at times without the patient's knowledge. For historians, eugenic sterilization has become a set-piece illustrating the hubris of the high-modernist faith in medical authority with sinister echoes of the Nazi quest for racial purity. In this poignant study of the state of Minnesota's eugenic sterilization program, Molly Ladd-Taylor provides a sobering reassessment anchored to evidence that gives voice to the heartbreaking experience of the men and women who became the subjects of Minnesota's sterilization program. Ladd-Taylor argues that so much scholarly attention has focused on outliers, such as states with high-sterilization rates or zealous eugenicists, that valuable narratives about the average experience has been overlooked. Minnesota provides an ideal case study because it sterilized a modest number of people (2,350) more or less on a voluntary basis with few active partnerships between the program and the eugenics movement. As Ladd-Taylor argues, this book is a social history focused on the routine "social welfare policies that aimed to solve the problems of poverty, sex, and single motherhood by 'fixing' the poor" (p. 2). As such, Ladd-Taylor's research is part of a revision among scholars to highlight how American understandings of the connections between poverty, behavior, and reproduction have changed and persisted into the present. Eugenic sterilization was born out of the crises of the Gilded Age, when growing urban populations composed of ethnically diverse immigrants stretched social support systems to the breaking point during the boom and bust cycles that characterized the time period. Ladd-Taylor argues that two Gilded Age ideas would inform eugenic policies throughout the twentieth century. The first was the identification of a two-tiered understanding of poverty and an impulse to distinguish between the "deserving" and "undeserving" poor through "scientific charity" (p. 27). The second was the invention of childhood as a distinct and fragile stage of development necessary to producing fully realized citizens and workers. Scientific charity gained coherence in the 1870s and 1880s with the publication of Richard Dugdale's 1877 eugenic study of the Jukes family and Frederick Wine's demographic analysis of "defective" classes for the 1880 census. These two studies confidently tied poverty to genetics and attempted to quantify the economic and social costs of, in Wine's phrasing, the "morphology of evil" (p. 29). In creating its first poor law in 1864, the state of Minnesota recognized a public responsibility for addressing poverty that blossomed into a flirtation with socialism during the People's Party movement of the 1890s, which advocated systemic reform of society to uplift the deserving poor. By the Progressive Era, scientific charity had merged with eugenics to focus on the childhood environment. For Ladd-Taylor, the passage of Minnesota's Children's Code in 1917 served as "the foundation of its eugenic sterilization law" because it encoded these earlier understandings [End Page 435] into law and empowered the state to "impose certain behavioral and economic standards on the poor" (pp. 26, 56). As the Children's Code swamped Minnesota's aging institutions, Ladd-Taylor details how welfare officials viewed sterilization as a solution to poverty. Ladd-Taylor is at her best when situating Minnesota's experience in the larger context of American history, such as the Progressive Era imperative to quantify and categorize every facet of economic and social life. As Victorian social norms of female behavior clashed with the ascendant dance hall culture of the Jazz Age, the more traditional social welfare agents at the Minnesota Board of Control came into conflict with such hardcore eugenicists as Guy C. Hanna, the superintendent at the Faribault School for the Feebleminded. Ladd-Taylor reconstructs these battles by mining rich local collections. While Hanna believed that feeblemindedness was a...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".