Policing harm reduction: A case study of who counts as human
Bibliographic record
Abstract
The debate over whether or not to have police officers carry naloxone was waged long enough in Windsor, Ontario that it became referred to in the local media as "the naloxone issue." Compared to other cities and towns in the Canadian province, Windsor was slow to equip police officers with the life-saving drug that reverses the effects of opioid overdose. In this paper, we document the unhurried development of this particular harm reduction measure as it unfolded over the course of several years. We draw on theories of human disposability in our analysis of the debates and discussions conveyed through media coverage of the naloxone issue. In doing so, we develop an account for the resistance to a rapid implementation of harm reduction in Windsor, especially given the compounded crises of escalating opioid-related deaths and the COVID-19 pandemic. Examining the logics underpinning major, minor, and left unsaid positions taken in discussions and debates on the naloxone issue as conveyed through press media, we show how, ideologically, the lives of people who use drugs (PWUD) are not only less valued, but made into wasted life. Underlying police and city administrations' delay in equipping police officers with naloxone is an understanding of PWUD as disposable. The naloxone issue was turned into a controversy, demonstrating that the humanity of PWUD is up for debate. In contrast, the humanity of police officers is regarded as self-evident. We conclude by arguing that the eventual decision to equip all Windsor police officers with naloxone was made in the name of police safety, in particular as a way of protecting police from homeless bodies, constructed as both dangerous and drug-using, thereby revealing that the crux of the controversy was actually about whose life is worth protecting.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.052 | 0.026 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".