Risk Terrains of Illicit Drug Activities in Durham Region, Ontario
Bibliographic record
Abstract
Street-level drug activities pose a serious problem for communities, and exploring the environmental context of drug crimes is one important aspect of the increasing problem in Canada. This study examined the urban backcloth of illicit drug activities in the Durham Region, Ontario. Drawing on the locations of 5,297 drug arrests between 2011and 2013, along with 6,291 surrounding physical features in the environment, the risk terrain modelling framework guided the analyses, which revealed that the risk of drug crimes varies by context and time. Similar to previous research in the United States and the Netherlands, the authors found that 11 out of 18 correlates were significantly associated with drug crimes. Unlike other study settings, the locations of alcohol sales and service did not predict the occurrence of drug crimes in the Durham Region. In addition, the risk clusters differed when the same correlates were modelled for incidents of each year separately. The models provided a valid prediction from one year to the next. Nearly 85% of all places with illicit drugs arrests in 2012 and 2013 overlapped with high-risk places of 2011 and 2012, respectively. The resulting risk map informs practitioners and policy makers on where to focus resources in the region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".