The Case for Policy Reform in Cannabis Control
Bibliographic record
Abstract
This is not a debate on the harms of cannabis. These are well-known. Acute effects include accidents with motor vehicles or machinery, and adverse reactions. In the longer term, cannabis has been associated with cognitive impairment and psychosis, although not consistently, and direct causality is more difficult to establish than for acute effects. It is possible that cannabis precipitates schizophrenia in those who are predisposed through a personal or family history. The relation is also 2-way, with cannabis being the most commonly used illicit drug in those with schizophrenia. Rather, this is a debate of how best to address the mental health consequences of cannabis. More specifically, it is a debate about overreliance on just one supply-side strategy, prohibition, at the expense of demand-side approaches, such as education, treatment, or prevention. This is of particular relevance to Canada as proposed legislation (Bill C-26) will place an even greater emphasis on law enforcement. This article discusses the origins and effectiveness of prohibition, and argues that we should apply the lessons from alcohol or tobacco control to cannabis.
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 | 0.048 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.046 | 0.034 |
| Insufficient payload (model declined to judge) | 0.023 | 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".