Dynamics between opioid use, unemployment, and property crime in Vancouver, Edmonton, Calgary, and Toronto
Bibliographic record
Abstract
Canada is currently experiencing a national opioid crisis, which has many associated negative effects on the individual and society. Empirical evidence has confirmed a link between illicit opioid use and property crime, committed to finance illicit drug consumption among users who have no other revenue streams. Across Canada, governments have responded in various ways to the opioid crisis at hand. The drug-crime link has been put under additional scrutiny after provincial governments in Alberta and Ontario have initiated reviews of the community effects—including property crime—around safe consumption sites. However, only few studies have attempted to understand the combined dynamics among illicit opioid use, property crime, and the state of the local economy. From a database of opioid-related overdose rates, rates of break and enters and thefts from vehicles, and unemployment rates in Vancouver, Edmonton, Calgary and Toronto, correlation and regression techniques were applied to understand the relationship between the variables. The results show significant variation among the cities studied, which in some cases suggest other drivers affect property crime rates, and that the relationship between opioid use and property crime may be negative in other cases. The findings may be used to alleviate community concerns regarding harmreduction initiatives as a response to the opioid crisis. However, the inconsistent results primarily call for further studies to explore whether connection between illicit substance abuse and property crime in the wake of illicit fentanyl proliferation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".