Policing the Overdose Crisis
Bibliographic record
Abstract
The opioid overdose crisis in Canada continues to claim the lives of people who use drugs (PWUD). Historically, Canadian crime policy has prioritized crime control forms of surveillance, interdiction and punishment in response to drug use. More recently, harm reduction measures have gained traction, including safe consumption sites (SCS) and police officer use of Naloxone to assist PWUD who have overdosed on opioids. The effectiveness of harm reduction efforts, however, is to some degree contingent on their embrace or acceptance by police agencies and officers. This paper is based on research conducted on the two largest city-level police services in Alberta, Canada. We conducted 94 interviews with officers and had 1,406 officers complete a quantitative survey on issues relating to illicit drugs, overdoses, and fentanyl. Our findings show that police officers generally see opioid use as a serious problem and are concerned about the dangers they face when dealing with PWUD. There is also considerable confusion about the nature and severity of these dangers. Even so, attitudes appear to be shifting and some police officers are changing their practices. In general, our research documents a softening of police attitudes in Canada towards SCS facilities and harm reduction more generally. This greater embrace of a public health orientation could improve the lives of PWUD and their interaction with law-enforcement in Canada. Given the prospect that fentanyl and related synthetic opioids will continue their global spread, these findings should be of interest to an international audience of scholars, police, and healthcare officials.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".