Human Rights Standards Relevant to Mental Health and How They can be Made More Effective
Bibliographic record
Abstract
François Crépeau and Anne-Claire Gayet argue that improving the effectiveness of human rights standards relevant to mental health depends firstly on being aware of the existing framework. This they do by considering the specific responsible institutions – international and regional treaty bodies which receive periodic reports, produce ‘soft law’, hear individual and collective complaints, and undertake on-site visits; the procedures of independent experts and UN Special Rapporteurs; the work of international courts and tribunals, national judicial tribunals and human rights institutions, and other inquiries and investigations, both at international and national levels. They provide examples about the work of these bodies. They also explore how human rights standards can be actively promoted and protected, through cross-sectoral cooperation, inter-institutional consultation, and mobilizing civil society and human rights education for all. These arguments particularly draw on the Canadian context.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".