Atmospheric pressure: Russian drug policy as a driver for violations of the UN Convention against Torture and the International Covenant on Economic, Social and Cultural Rights.
Bibliographic record
Abstract
BACKGROUND: Responding to problematic drug use in Russia, the government promotes a policy of "zero tolerance" for drug use and "social pressure" against people who use drugs (PWUD), rejecting effective drug treatment and harm reduction measures. OBJECTIVE/METHODS: In order to assess Russian drug policy against the UN Convention Against Torture and the International Covenant on Economic, Social, and Cultural Rights, we reviewed published data from government and non-governmental organizations, scientific publications, media reports, and interviews with PWUD. RESULTS: Drug-dependent people (DDP) are the most vulnerable group of PWUD. The state strictly controls all aspects of drug dependence. Against this background, the state promotes hatred towards PWUD via state-controlled media, corroding public perception of PWUD and of their entitlement to human rights. This vilification of PWUD is accompanied by their widespread ill-treatment in health care facilities, police detention, and prisons. DISCUSSION: In practice, zero tolerance for drug use translates to zero tolerance for PWUD. Through drug policy, the government deliberately amplifies harms associated with drug use by causing PWUD (especially DDP) additional pain and suffering. It exploits the particular vulnerability of DDP, subjecting them to unscientific and ideologically driven methods of drug prevention and treatment and denying access to essential medicines and services. State policy is to legitimize and encourage societal ill-treatment of PWUD. CONCLUSION: The government intentionally subjects approximately 1.7 million people to pain, suffering, and humiliation. Aimed at punishing people for using drugs and coercing people into abstinence, the official drug policy disregards the chronic nature of drug dependence. It also ignores the ineffectiveness of punitive measures in achieving the purposes for which they are officially used, that is, public safety and public health. Simultaneously, the government impedes measures that would eliminate the pain and suffering of DDP, prevent infectious diseases, and lower mortality, which amount to systematic violations of Russia's human rights obligations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".