The future of the international drug control system and national drug prohibitions
Bibliographic record
Abstract
A major impediment to any nation abandoning the policy of drug prohibition has been the fact that international drug treaties to which the majority of United Nations (UN) member states are signatory prohibit the non-medical use of amphetamines, cannabis, cocaine and heroin. The future of these treaties is now uncertain because of decisions by Uruguay, eight US states and Canada to legalize cannabis use. This paper: (1) provides a brief account of the international drug control treaties; (2) outlines the major criticisms of the treaties; (3) analyses critically proposals for treaty reform; and (4) provides a personal view on policies that nation states could adopt to minimize the harms from the use of cannabis, party drugs and hallucinogens, opioids, stimulants and new psychoactive substances. It is argued that: a major risk of cannabis legalization in the United States is promotion of heavy use and increased harm by a weakly regulated industry; some cautious national experiments with the regulation of party drugs and hallucinogens would be informative; a strong case remains for prohibiting the nonmedical use of opioids while mitigating the adverse effects that this policy has on opioid-dependent people; stimulant legalization will probably increase problem use but prohibition is difficult to enforce, highlighting the urgency of finding better ways to reduce demand for these drugs and respond to problem users; and that it is unclear what the best approach is to reducing possible harms that may arise from the use of new psychoactive substances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".