Bibliographic record
Abstract
En 1928, la province canadienne de l'Alberta a adopté une loi sur la stérilisation sexuelle et, jusqu'à son abrogation en 1972, plus de 2,800 personnes ont été stérilisées. De ce nombre, les femmes ont été surreprésentées. Cet article examine comment des regards changeants portés sur l'eugénique ont conduit à une transformation subtile d'où a émergé un système à deux volets qui a ciblé des hommes ayant une déficience mentale, souvent un danger pour la société, et des femmes normales du point de vue mental mais anormales moralement qui ont accepté la stérilisation. Le geste s'est avéré un succès en ce qui concerne le type et le nombre de personnes stérilisées, et la pérennité du programme. Alberta, Canada, passed a Sexual Sterilization Act in 1928 and up until its repeal in 1972, over 2,800 people were sterilized. Women were overrepresented in the number of sterilizations performed. This paper explores how changing understandings of eugenics led to a subtle transformation which resulted in a “two‐pronged” system that targeted mentally defective men, often a danger to society, and mentally normal but morally abnormal women who consented to sterilization. The end result was success for the movement in terms of the types and numbers of people sterilized, and in the longevity of the program.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".