Impacts of Canada's cannabis legalization on police‐reported crime among youth: early evidence
Bibliographic record
Abstract
AIMS: Canada's 2018 Cannabis Act allows youth (age 12-17 years) to possess up to 5 g of dried cannabis (or equivalent) for personal consumption/sharing. This study assessed whether the Cannabis Act was associated with changes in police-reported cannabis offences among youth in Canada. DESIGN: Time series model using national daily criminal incident data from January 1, 2015-December 31, 2018 from the Canadian Uniform Crime Reporting Survey (UCR-2). Seasonal autoregressive integrated moving average time series models, stratified by sex, assessed the relations between legalization and youth cannabis-related offences. SETTING: Canada, 2015-2018. CASES: Police-reported cannabis-related offenses among youth age 12-17 years (male, n = 32 178; female, n = 9001). MEASUREMENTS: Outcomes: police-reported cannabis-related crimes, property crimes, and violent crimes. Covariate: calendar-month. FINDINGS: For females, legalization was associated with a step-effect decrease of 4.56 (95% confidence interval [CI] = 3.32, 5.81; P < 0.001) police-reported cannabis-related criminal offences per day, an effect equivalent to a 64.6% (standard error [SE] = 33.5%) reduction. For males, legalization was associated with a drop of 12.73 (95% CI = 8.82, 16.64; P < 0.001) cannabis-related offences per day, equaling a decrease of 57.7% (SE = 22.6%). Results were inconclusive as to whether there were associations between cannabis legalization and patterns of property crimes or violent crimes. CONCLUSIONS: Implementation of the Cannabis Act in Canada in 2018 appears to have been associated with decreases of 55%-65% in cannabis-related crimes among male and female youth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".