‘Valuable Lives to Save’ vs. ‘Babysitting These People While They Try to Kill Themselves’: Changing Police Attitudes Towards Safe Consumption Sites
Bibliographic record
Abstract
Abstract North American police responses to the ‘drug issue’ have long been shaped by a crime control rather than a harm reduction imperative. Consequently, police officers’ responses to safe consumption sites (SCSs), where people who use illicit drugs can reduce personal health risks by administering previously obtained drugs in the presence of trained staff, were initially hostile. This paper draws on interview data from police officers in two western Canadian cities to highlight an apparent softening in attitudes, perhaps due to the current fentanyl-driven drug poisoning crisis. While some officers clearly recognized their public health benefits, others accepted SCSs, acknowledging the futility of a continued ‘war on drugs’. Some voiced reservations about SCSs, but not because of a generic ‘drugs are bad’ sentiment. Rather, they worried about specific downstream implications for communities and police work. These findings, reflecting apparent changes in police officers’ responses to SCSs, are discussed in the context of contemporary debates about police culture and the possibilities and desirability of pursuing police reform.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".