Harm reduction as anarchist practice: a user's guide to capitalism and addiction in North America
Bibliographic record
Abstract
In spite of its origins as an illegal, clandestine, grassroots activity that took place either outside or in defiant opposition to state and legal authority, there is growing evidence to suggest that harm reduction in North America has become sanitized and depoliticized in its institutionalization as public health policy. Harm reduction remains the most contested and controversial aspect of drug policy on both sides of the Canada–US border, yet the institutionalization of harm reduction in each national context demonstrates a series of stark contrasts. Drawing from regional case study examples in Canada and the US, this article historically traces and politically re-maps the uneasy relationship between the autonomous political origins of harm reduction, contemporary public health policy, and the adoption of the biomedical model for addiction research and treatment in North America. Situated within a broader theoretical interrogation of the etiology of addiction, this study culminates in a politically engaged critique of traditional addiction research and drug/service user autonomy. Arguing that the founding philosophy and spirit of the harm reduction movement represents a fundamentally anarchist-inspired form of practice, this article concludes by considering tactics for reclaiming and re-politicizing the future of harm reduction in North America.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| 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".