FOSTERING COMPLIANCE WITH WOMEN’S RIGHTS IN THE INTER-AMERICAN SYSTEM
Bibliographic record
Abstract
This article explores why the practice of states in the Americas has been slow to comply with the human rights of women since the adoption of the American Declaration of the Rights and Duties of Man in 1948. The article explains that since states tend to obey international law as a result of repeated interaction among transnational actors, a first step toward fostering compliance is to empower more actors concerned with women, particularly marginalized women, to participate in the process. A next step is to explore how the available forums at both the regional and international levels have been and could be used to apply human rights to the harms experienced by women. Consistent with this step, the article reviews the case law concerning women developed by the Inter-American Court of Human Rights, the Inter-American Commission on Human Rights and the international treaty bodies. A third step toward fostering compliance is to assess strategies of how best to internalize women’s rights into domestic laws, policies and practices. Pursuant to this step, the article identifies some of the key concluding observations in the country reports of treaty bodies, and notes that most states have yet to change their laws to comply with these observations. The article concludes by examining how sanctions and rewards, or a mixture of the two, could be used to improve compliance with the human rights of women in the future.
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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 | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| 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".