Addressing Human Rights Abuses against People Who Use Drugs: A Critical Role for Human Rights Treaty Bodies and Special Procedures
Bibliographic record
Abstract
Since the 1980s, United Nations (UN) Member States have seen issues of drug policy predominantly as problems of law enforcement and security, alienated from other UN priorities, including human rights. However, developments in the UN and global challenges, like HIV/AIDS have made have made it more difficult to separate drug policy from its impact on the health and human rights of people who use drugs, particularly drug dependent people. As a result, several UN and human rights bodies have all begun to include human rights considerations in their policy documents. Despite these positive developments, the UN human rights system lags behind and UN human rights bodies fall short in addressing human rights violations against people who use drugs. To remedy this shortfall, this article first explains the need for new guidelines on drug policy and human rights by describing the impact of drug policy on human rights and the ways in which UN human rights bodies have thus far failed to adequately address violations suffered by people who use drugs. The authors join other international organizations and activists calling for the adoption of the guidelines (a human rights impact assessment tool) to provide the UN human rights treaty bodies and special procedures with clear guidance on how to assess drug policy issues through the prism of international human rights standards.
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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 | 0.232 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.084 |
| Scholarly communication | 0.033 | 0.045 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.050 | 0.085 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".