Forced or Coerced Sterilization in Canada: An Overview of Recommendations for Moving Forward
Bibliographic record
Abstract
Between 2015 and 2019, over 100 Indigenous women from six provinces and two territories have come forward to say that they were forced or coerced to undergo a sterilization procedure in Canada. Despite this, government action is lacking. Through this paper, the research team aims to collect and synthesize the recommendations that have been made in response to the recent cases of forced or coerced sterilization of Indigenous women in Canada. Through a secondary analysis of data, we outline the findings of a thematic analysis of 162 recommendations from four selected sources: (a) Tubal Ligation in the Saskatoon Health Region: The Lived Experience of Aboriginal Women, an external review by Senator Yvonne Boyer and Dr. Judith Bartlett, July 22, 2017; (b) a meeting of the Senate Committee on Human Rights, April 3, 2019; (c) meetings of the House of Commons Standing Committee on Health, June 13 and 18, 2019; and (d) a letter from Bill Casey, Member of Parliament and Chair of the House of Commons Standing Committee on Health, to three federal ministers, August 2, 2019. Seven themes emerged following the thematic analysis of the 162 recommendations: (a) Services and Supports (b)Accountability, (c) Training and Education, (d) Legislation and Policy, (e) Criminalization, (f) Data Collection, and g) Investigation. These themes represent seven areas where immediate government action is required to meaningfully and appropriately respond to the recent cases of forced or coerced sterilization of First Nations, Inuit, and Metis women in Canada.
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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 | 0.067 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".