Key crime- and public safety-related results of non-medical cannabis legalization policy in Canada: a targeted evidence summary
Bibliographic record
Abstract
Purpose Cannabis policies are increasingly being liberalized, including the de jure legalization of non-medical cannabis use and supply in Canada (2018) implemented toward improved public health and safety outcomes. While health outcomes have shown mixed results, less attention has been given to crime- and public safety-related outcomes. The purpose of this paper is to provide a targeted literature/data summary on select main crime- and public safety-related outcomes associated with cannabis legalization policy in Canada as implemented in 2018. Design/methodology/approach The authors conducted a targeted literature/data review focusing on key, publicly available outcome indicators associated with cannabis legalization in Canada in three main domains, obtained from both academic (e.g. journal) and “grey” (e.g. survey/government reports) literature/data sources: cannabis crime and enforcement; cannabis-impaired driving and related motor-vehicle-crash involvement; and cannabis markets and sourcing. The data draw on targeted searches in related areas, are narratively summarized by topic and briefly discussed for implications and knowledge gaps. Findings The results of this study suggest that cannabis – and specifically possession – crimes have substantially decreased; less is known about enforcement patterns for the remaining cannabis offenses or impacts on other potentially cannabis-related crimes. The prevalence of cannabis-impaired driving appears to be declining, while levels of cannabis involvement in motor vehicle crashes appear to have increased. Legal cannabis markets and the legal sourcing of cannabis among consumers have steadily increased to involve approximately three-quarters of acquisition activities, implying major reductions of illegal cannabis retail markets. Conversely, data on the evolution of illegal cannabis production and supply markets in legalization policy contexts is highly limited and may include displacement effects. Practical implications *Cannabis legalization has been implemented toward public health and safety improvement objectives, including in Canada (2018). For key outcomes, legalization has been associated with substantive reductions in enforced cannabis offenses among adults and youth. The prevalence of cannabis-impaired driving may be declining, but levels of cannabis-related motor-vehicle-crashes have been increasing. Cannabis sourcing has gradually but steadily shifted from illegal to legal sources among the majority of consumers; legalization’s effects on cannabis production and supply markets are largely unclear. Data on crime-related outcomes of legalization need to be systematically expanded, as they form an essential aspect of comprehensive policy impact assessments. Originality/value While available data suggest improvements in some (e.g. health-related) areas, there is a need for comprehensively expanded research on legalization’s impacts on key crime- and safety-related indicators, required for consideration in overall, integrated policy assessments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.312 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".