Confronting Violence Against Women - A Brief Guide to International Human Rights Law for Canadian Advocates
Bibliographic record
Abstract
TABLE OF CONTENTS BACKGROUND AND PURPOSE OF THIS PAPER CANADIAN RIGHTS LEGISLATION AND MECHANISMS The Canadian Constitution Canadian Human Rights Legislation LEGAL LITERACY – DOMESTIC AND INTERNATIONAL LAW Can international rights treaties help Canadian women? INTERNATIONAL HUMAN RIGHTS LAW Declarations, treaties, conventions and protocols THE ENFORCEMENT OF HUMAN RIGHTS STANDARDS A BRIEF HERSTORY: VIOLENCE AGAINST WOMEN AS AN INTERNATIONAL HUMAN RIGHTS ISSUE ANTI-VIOLENCE MECHANISMS 1. Example of a Monitoring Procedure: The Special Rapporteur on Violence Against Women 2. CEDAW: The Women’s Convention and The Committee on the Elimination of Discrimination Against Women 3. CEDAW Optional Protocol 4. Convention on the Rights of the Child 5. International Criminal Court 6. Regional Human Rights Systems - The Inter-American Human Rights System, Organization of African States, Council of Europe 7. Other International Organizations in the UN System: IMF, World Bank, WTO EXAMPLES OF HOW CANADIANS HAVE USED UN TREATIES 1. Lovelace Complaint: Optional Protocol to the International Covenant on Civil and Political Rights 2. OAITH Submission to the Special Rapporteur on VAW 3. The Ontario People’s Reports by LIFT 4. CEDAW Shadow Reports SUGGESTED ACTION FOR ADVOCATES AND ACTIVISTS 1. Canada's ratification of the CEDAW Optional Protocol 2. Moving governments to take action 3. Use of International Mechanisms in Domestic Litigation 4. The World Conference Against Racism (WCAR) 2001 5. Supportive strategies for building the culture of human rights CONCLUSION
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.012 |
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".