Accelerating the eradication of violence against women: prospects of an international treaty and Canada’s role in supporting global efforts
Bibliographic record
Abstract
Gender-based violence against women has been recognized as one of the most prevalent human rights violations worldwide. Pandemic levels of violence in every nation, including Canada, have led to a public health and economic crisis. However, the Convention on the Elimination of All Forms of Discrimination against Women, the principal United Nations treaty addressing the issue, has no specific mention of violence within its provisions. In 2021, the United Nations Secretary-General called for enhanced global solidarity and multilateralism to accelerate the eradication of violence against women. The key focus of this paper is whether an international treaty on violence against women can accelerate this change and whether Canada should promote its adoption. Through the analysis of academic and gray literature, including United Nations documentation, research reports, government documents, and news articles, this paper examines the scope of the issue globally and within Canada, analyzes the strengths and limitations of existing international frameworks, discusses the prospects of an international treaty, and considers Canada’s role in supporting global efforts. This paper concludes that a legally binding global instrument on violence against women offers more potential for transformative change than continuing with the status quo and that the Government of Canada should advance its consideration at the global level. This paper concludes with further recommendations about how Canada can bolster global efforts to accelerate the eradication of gender-based violence against women.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".