Ericka Dyck and Maureen Lux, <i>Challenging Choices: Canada’s Population Control in the 1970s</i>
Bibliographic record
Abstract
Authors Ericka Dyck and Maureen Lux examine the awkward alliance of eugenics, population control and birth control in relation to four groups—Indigenous women, persons with intellectual disabilities, men and teen girls—in the 1970s. During this decade, Malthusian-influenced fears of overpopulation in the Global South led to the repurposing of birth control in the Global North. Certainly, late twentieth-century anxiety over a population explosion was a major factor in the liberalisation of Canada’s birth control laws in 1969, just as eugenically driven concerns over race suicide in the early twentieth century had led to the promulgation of coercive sterilisation laws in the provinces of Alberta and British Columbia. Nazi Germany’s catalogue of horrors challenged the eugenics movement. However, the long shadow that eugenics cast, even in provinces that did not pass coercive sterilization laws explains why, in a country with a large land mass and a small population, the prospect of...
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".