Eugenics in Comparative Perspective: Explaining Manitoba and Alberta’s Divergence on Eugenics Policy, 1910s to the 1930s
Bibliographic record
Abstract
This dissertation compares eugenics in Alberta and Manitoba in order to explain their divergence on sexual sterilization policy. Alberta implemented a Sexual Sterilization Act in 1928, while Manitoba rejected similar legislation in 1933. This thesis shows that Manitobans actively engaged with national and international discussions and debates about eugenics despite a lack of an official eugenics program. Eugenics was hardly monolithic and by focusing attention only on provinces with formal eugenics programs, historians miss how eugenic ideas manifested themselves in provinces without sterilization legislation, for example in mental institutions, in educational programs, and in child welfare policies. Lack of legislation does not necessarily mean that there was a lack of enthusiasm for eugenic measures. This dissertation brings a comparative aspect to the history of eugenics in Canada and demonstrates the ways in which eugenic policy was influenced at various levels by an emerging professional class of psychiatrists, by grassroots organizations, by religious groups, and by the unique local conditions including demographic, cultural, and political factors. I argue that Manitoba and Alberta shared similar concerns about “race degeneration,” “defective” immigrants, and the economic costs of running institutions, but there were important subtle differences in the political contexts of the two provinces. These differences served to empower the opposition elements to sexual sterilization in Manitoba, while in Alberta it served to empower grassroots organizations that were adjacent to the government, and at the same time weaken any political critics. A comparative perspective is valuable in understanding the history of eugenics in Canada especially because of regional differences but more importantly because each province has its own historical, social, and political traditions that help illuminate their distinct approaches to eugenics. The importance of a comparative perspective to the history of eugenics in Manitoba and Alberta is that it gives us insight into the political and cultural debates that occurred during the interwar period in order to better understand the forces at play and discussions regarding eugenics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.025 | 0.027 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".