Harm Reduction— And What Keeps Us From Embracing It Fully
Bibliographic record
Abstract
In this Review essay, we examine some of the latest and needed scholarship on harm reduction: Travis Lupick’s Fighting for Space: How a Group of Drug Users Transformed One City’s Struggle with Addiction (2018); Jarrett Zigon’s A War on People: Drug User Politics and a New Ethics of Community (2019); Kimberly Sue’s Getting Wrecked: Women, Incarceration, and the American Opioid Crisis (2019); and Nancy Campbell’s OD: Naloxone and the Politics of Overdose (2020). Our authors present us with intimate windows into a diverse array of geographies, peoples, and technologies—from women’s jails, prisons, and community treatment programmes in Massachusetts to Vancouver’s downtown; from Copenhagen’s safe injection sites to prisons in Scotland. While varied in methods and approaches, these works unequivocally push for alternative imaginings to what one of Campbell’s protagonists dubs the ‘North American disaster’. Harm reduction is front and centre to these authors’ envisioning of a kinder, more loving, and more accepting future. Embracing harm reduction both requires and initiates a radical rethinking of how drug use is viewed, and our authors have given us crucial insight and analyses into how such reorientations are possible. We encourage continued scholarship on this topic, especially on non-Western options.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.063 |
| Scholarly communication | 0.019 | 0.037 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".