Factors contributing to frequent police contact among young people: a multivariate analysis including homelessness, community visibility, and drug use in British Columbia, Canada
Bibliographic record
Abstract
There is increasing recognition and attention towards the patterns of police encounters with citizens. In this study, we examine the determinants of being stopped and questioned by the police among a heterogenous sample of adolescents and young adults, who were either people who use drugs and a comparison group, in three non-metropolitan areas of British Columbia, Canada. We conducted bivariate and multivariate analyses to identify the unique variation of frequent police encounters based on demographic characteristics and potential confounders. Of 448 young people, 92.0% reported at least one event where police stopped and questioned them in the past five years, and half (49.8%) reported frequent (four or more) police encounters in this time frame. The demographics of race, age, and gender were not significant in the analyses, whereas weekly illicit drug use, homelessness, and high visibility in the community were significantly related to frequent police encounters. Findings suggest that, controlling for demographic variables, young people who have precarious housing, use drugs, and have higher community visibility are at higher risk of police contact. Our findings also show when street involvement and drug use are controlled for, race does not determine police encounters.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".