The impacts of cannabis legalization on organized crime in Ontario and British Columbia
Bibliographic record
Abstract
This article presents a qualitative study of the impacts of cannabis legalization on organized crime in two of Canada’s largest provinces – Ontario and British Columbia. Utilizing a modified snowball sampling methodology, we conducted semi-structured interviews with 23 subject matter experts in law enforcement, journalism, law, public service, and the private sector. Our findings are complex and reflect a sophisticated, rational response by organized crime to a key legislative event. First, Health Canada’s personal/designated production registrations remain in place, which since 2001 have played a crucial role for allowing some patients access to medical cannabis. However, according to nearly every law enforcement officer interviewed, criminal entities continue to abuse this unique system by obtaining licences under false premises and diverting surplus product into the illicit market. Second, while the domestic market for illicit cannabis has likely declined, many interviewees claimed that organized crime groups adjusted operations by maintaining production levels and diverting shipments into the United States, and also by exploiting relationships with some Indigenous communities. This has contributed to deteriorating public health and safety outcomes within some Indigenous communities. Finally, we registered the belief of many interviewees that the most sophisticated criminal groups have begun shifting attention toward opiate production and distribution – potentially in response to both a smaller domestic market for cannabis, and also the skyrocketing demand for opioids. Our results reflect the unique character of Canadian geography, institutions and electoral politics, notably close proximity to the United States, federal-provincial division of powers, the evolving legacy of colonialism, and a unique series of legal precedents.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".