Overpoliced and Underrepresented: Perspectives on Cannabis Legalization From Members of Racialized Communities in Canada
Bibliographic record
Abstract
Historically, overpolicing of some racialized and Indigenous groups in Canada has resulted in unequal application of drug laws contributing to disproportionate rates of charges and convictions in these populations. Criminal records severely and negatively impact an individual's life and can perpetuate cycles of poverty and socioeconomic disadvantage. On October 17, 2018, Canada legalized cannabis production, distribution, sale, and possession for non-medical purposes. Advocates of criminal justice reform have raised concerns that Indigenous and racialized people may not equitably benefit from legalization due to unequal police surveillance and drug enforcement. These groups are among priority populations for research on cannabis and mental health, but their views on cannabis regulation have been largely absent from research and policy-making. To address this gap, we asked self-identified members of these communities about their lived experiences and perspectives on cannabis legalization in Canada. Between September 2018 and July 2019, we conducted semistructured interviews and focus groups with 37 individuals in Québec, Ontario, Alberta and British Columbia. During this phase of early cannabis legalization, participants responded to questions about anticipated public health risks and benefits of legalization, how their jurisdiction is responding to legalization, and what community resources would be needed to address legalization impacts. We conducted a thematic analysis and identified five major themes in the data related to race and early cannabis legalization: overpolicing of racialized communities, severity of penalties in new cannabis legislation, increased police powers, and underrepresentation of racialized groups in the legal cannabis market and in cannabis research. Participants discussed opportunities to support cannabis justice, including establishing priority licenses, issuing pardons or expunging criminal records, and reinvesting cannabis revenue into impacted communities. This work begins to address the paucity of Indigenous and racialized voices in cannabis research and identifies potential solutions to injustices of cannabis prohibition.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.057 | 0.020 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".