“Arrest Us or Consider Us Healthcare”: Towards Understanding The Counter-Conducts of Overdose Prevention Ottawa
Bibliographic record
Abstract
North America has seen a dramatic increase in overdose deaths since 2016.Cities across Canada have responded to the growing opioid epidemic in a myriad of ways.Safe consumption sites have been one of these interventions, however the uptake of harm reduction services and safe injection sites has been inconsistent across regions leading to a patchwork of care.In the absence of access to sanctioned services, people who use drugs and grassroots activists have developed and administered a host of harm reduction services including unsanctioned injection sites.Ottawa, Ontario was host to one such unsanctioned site run by an organization known as Overdose Prevention Ottawa (OPO).This work provides a case study on OPO, examining their direct actions and rejections of medical practices.Through seven semi-structured interviews with OPO organizers and managers of sanctioned sites, this project aimed to interrogate the contestations of practices, knowledges, and logics which govern substance use and related care.The resulting analyses pertain to the contestations of practices from outside the site, as well as within, exploring relations visibility and invisibility of drug use and public consumption, positioning of medical and experiential expertise, and how protest actions engaged external onlookers.Care practices within the site highlight non-totalizing rejection of medical logics and practices, and articulated care on the fringes of medical regimes.This positioning is explored in a comparative analysis of sanctioned and unsanctioned replacement opioid programs.Ultimately these analyses provide insight into the counter-conducts of Overdose Prevention Ottawa, suggesting alternative practices and logics of care for people who use drugs.through critical reflection and contributed meaningfully to my capacity to undertake this research.Further, I wish to thank Dr. Carlos Novas for his advice
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.057 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| 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".