Critical studies of harm reduction: Overdose response in uncertain political times
Bibliographic record
Abstract
North America continues to witness escalating rates of opioid overdose deaths. Scale-up of existing and innovative life-saving services – such as overdose prevention sites (OPS) as well as sanctioned and unsanctioned supervised consumption sites – is urgently needed. Is there a place for critical theory-informed studies of harm reduction during times of drug policy failures and overdose crisis? There are different approaches to consider from the critical literature, such as those that, for example, interrogate the basic principles of harm reduction or those that critique the lack of pleasure in the discourses surrounding drug use. Influenced by such work, we examine the development of OPS in Canada, with a focus on recent experiences from the province of Ontario, as an important example of the impacts associated with moving from grassroots harm reduction to institutionalised policy and practice. Services appear to be most innovative, dynamic, and inclusive when people with lived experience, allies, and service providers are directly responding to fast-changing drug use patterns and crises on the ground, before services become formally bureaucratised. We suggest a continuing need to both critically theorise harm reduction and to build strong community relationships in harm reduction work, in efforts to overcome political moves that impede collaboration with and inclusiveness of people who use drugs.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.068 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".