Cannabis-related arrests and convictions in Canada: Differences by race/ethnicity, individual socioeconomic factors, and neighborhood deprivation
Bibliographic record
Abstract
Racialized individuals were disproportionately impacted by cannabis prohibition in Canada; however, the role of socioeconomic factors and neighborhood deprivation are not well understood. The current study examined race/ethnicity, individual socioeconomic factors, and neighborhood deprivation in relation to arrests and convictions for cannabis-related offenses. Repeat cross-sectional data were analyzed from two waves of the International Cannabis Policy Study (ICPS), a web-based survey conducted in 2019 (n = 12,226) and 2020 (n = 12,815) in Canada among those aged 16 to 65. Respondents were recruited through commercial online panels. Respondents’ postal codes were linked to the INSPQ deprivation index. Multinomial regression models examined the association between race/ethnicity, individual socioeconomic factors, neighborhood deprivation, and lifetime arrests or convictions for cannabis offenses. Overall, 4.4% of respondents reported a lifetime arrest or conviction for a cannabis-related offense. Black and Indigenous individuals had more than three times the odds of conviction than White individuals (AOR = 3.90, 95% CI = 2.07–7.35, p = <0.01; AOR = 3.24, 95% CI = 1.78–5.90, p = <0.01, respectively). Differences were still statistically significant after adjusting for cannabis use and socioeconomic factors; however, after adjusting for neighborhood deprivation, only the difference for Black individuals remained. Neighborhood deprivation was associated with cannabis-related convictions: the odds of a conviction among the “most privileged” and “privileged” neighborhoods were approximately half of those in the “most deprived” neighborhoods (AOR = 0.50, 95% CI = 0.29–0.86, p = 0.01; AOR = 0.50, 95% CI = 0.27–0.92, p = 0.03, respectively). Arrests and convictions for cannabis-related offenses were disproportionately higher among racialized individuals and those living in the most marginalized neighborhoods. Future research should examine whether inequities change following the legalization of recreational cannabis in Canada.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".