Police officer perceptions towards drug liberalization policies in the context of an overdose crisis in British Columbia, Canada
Bibliographic record
Abstract
Background British Columbia, Canada, is experiencing an overdose crisis that has pushed drug liberalization policies to the forefront of the response. This study examines police officers’ perceptions of enforcement and drug liberalization policies, including support for decriminalization and regulation or ‘safer supply’, in this context.Methods Qualitative interview data were collected in September-November 2020 from active police officers involved in drug law enforcement in British Columbia, prior to decriminalization reforms being introduced in the province. We conducted a thematic analysis of this data with a focus on police officer views towards drug enforcement and drug liberalization policies.Results Policing and reforms amid the overdose crisis has shaped officer perceptions and actions towards illegal drugs and drug policies. Although officers saw overdose as a health issue, these views coincided with a strong emphasis on supply-side policing, such as drug trafficking investigations. Policing continues to be entrenched in the overdose crisis, which has impacted the way police officers view drugs and drug use, particularly, their belief that current interventions to disrupt the illegal drug market are not working.Conclusion This study advances knowledge on the motivations for promoting drug enforcement, and enacting drug policies, in the context of an overdose crisis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".