How Well Is Cannabis Legalization Curtailing the Illegal Market? A Multi-wave Analysis of Canada’s National Cannabis Survey
Bibliographic record
Abstract
In 2018, the government of Canada legalized cannabis for non-medical use. In addition to safeguarding public health, the main objective was to divert profits from the illicit market and restricting its availability to youth. This dramatic shift in policy direction introduces new challenges for the criminal justice system due to the persistence of unlawful distribution among persons who refuse to abide by the new law. Continuing unlawful distribution is foreseeable, in part, because of stringent measures to reduce availability by targeting participants in the illegal market. Recognizing that the most heavy, frequent, users account for the majority of cannabis consumed—and are the group most likely to keep purchasing from dealers because of lower costs and easy access—the illegal market will continue to provide a substantial (albeit unknown) proportion of the total volume. The recent change in policy in Canada provides new opportunities for research to assess how legalization of cannabis affects its use and distribution patterns. The National Cannabis Survey (NCS), administered at three-month intervals, allows for multi-wave comparison of prevalence statistics and point of purchase information before and after legalization. Drawing on the NCS, this article examines the extent to which the primary supply source has changed across the provinces, controlling for other factors and consumer characteristics. Findings are interpreted with reference to studies of cannabis law reform in North America informing research and policy observers in these and other jurisdictions, undergoing or considering, similar reforms.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".