Local Drug–Crime Dynamics in a Canadian Multi-Site Sample of Untreated Opioid Users
Bibliographic record
Abstract
This multi-site study analysed self-reported involvement in property crime, drug dealing, and sex work, across five Canadian cities, among a sample of 677 illicit opioid and other drug users outside of treatment. First, we assessed drug-use patterns and the extent of illegal income-generating behaviour for each city. We then analysed factors and city interactions contributing to engagement in the respective criminal activities, including drug-use patterns, socio-economic characteristics, and other illegal activities. With this approach we explored city-specific patterns of crime prediction; thus we identified local drug–crime associations. The study sample was recruited by outreach and snowball methods and was assessed by standardized study protocols. Findings revealed substantial differences among the cities regarding both the extent and frequency of illegal activities. In regard to local differences, multiple logistic regression models revealed that crack use was strongly associated with property crime in Toronto, while cocaine use was strongly related with sex work in Montreal and Quebec City. This evidence points to local dynamics of drug cultures that are related to specific criminal activities. Implications for further research and intervention efforts are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".