Canada, cannabis legalization and uncertainty around the United Nations drug control conventions
Bibliographic record
Abstract
interventions such as needle syringe programmes, drug consumption rooms, methadone treatment programmes and heroin-assisted treatment have started carving off and regulating parts of the black market distribution system.It took a few generations to establish global drug prohibition.Developing a post-prohibition system will also take a long time.Drug prohibition has lasted for decades because while it failed as a policy it worked spectacularly well politically.However, as Herb Stein said: 'things that cannot last forever don't'.The politics of drug prohibition ensured that the economics of the drug market would undermine the policy and vice versa.What is needed is a policy that works both politically and economically.More compromise drugs, such as methadone, will be needed, which is acceptable to most people who would prefer heroin but which are also acceptable to authorities and the community.Until 1906, edible opium in small quantities was lawful in Australia, taxed and regulated, generating £60 000 government revenue per year.In the United States, Coca Cola contained cocaine until 1903.The principle of the state punishing its citizens for same-sex sex (between consenting adults in private) is now regarded widely as barbaric.The same principle should be applied to psychoactive drugs.Why is it still considered acceptable for the majority to punish harshly some citizens (absent harm to others) because they have minority drug tastes?If punishing citizens with minority drug tastes is rejected, as punishing citizens with minority sexual preferences has been, then the state would be required to identify the least-worst distribution system and begin implementing it.A regulated drug market would largely but incompletely undermine the black market.Trafficking quantities of unsanctioned currently illicit drugs would attract penalties just as trafficking quantities of legal drugs attracts penalties.Violence and robbery would continue to attract penalties.Whether or not drug consumption would increase after drugs are (mostly) regulated is much less important than whether harm would increase or decrease.The current evidence suggests that the relationship between drug consumption and policy liberalization is weak.The international drug control system is broken beyond repair.It is time to build a new system. Declaration of interestsNone.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.025 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".