Evaluation of decriminalizing small amounts of illicit drugs in Victoria, BC: A seasonally adjusted interrupted time-series analysis of police data
Bibliographic record
Abstract
BACKGROUND: In 2023, British Columbia (BC), Canada implemented a three-year pilot policy to decriminalize personal possession of a cumulative total of 2.5 g or less of opioids, cocaine, methamphetamine, and MDMA. Under this exemption, individuals found within the threshold are not subject to criminal charges, though possession above 2.5 g, possession in excluded locations (e.g., schools, airports), and trafficking remain criminal offences. This study evaluates the impact of this policy on police-reported drug-related offences, charges, and seizures in Victoria, BC. METHODS: Interrupted time-series analysis used police administrative data from the Victoria Police Department between January 2020 and December 2023. Monthly rates of drug-related offences, charges and seizures per 100,000 adults aged 15+ were analyzed across three policy phases: pre-announcement (run-in), policy awareness, and post-implementation. Autoregressive integrated moving average and mixed linear regression models were used to adjust for trend, seasonality and repeated measures. RESULTS: Offences and charges declined during the period prior to the implementation of decriminalization, but not during the decriminalization pilot. The mean weight of drugs seized per incident increased significantly post-implementation, but rates and proportions of seizures with cumulative drug weights <4.5 grams declined. CONCLUSION: Enforcement shifts preceded formal decriminalization, possibly reflecting anticipatory changes in policing practices. The post-implementation increases in seizure weights, alongside declining low-weight seizures, may indicate a reallocation of enforcement away from personal possession. To strengthen the impact of decriminalization, future efforts should prioritize clear policy communication, implementation training, and alignment between enforcement practices and public health goals.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".