Countering the institutionalization of harm reduction through critical pragmatism
Bibliographic record
Abstract
Although harm reduction practices have been increasingly adopted within health and social service systems globally over the last few decades, this process of institutionalization has not brought conceptual or theoretical clarity to the field of harm reduction. Instead, the growth of harm reduction practices within institutions has revealed the tension between grassroots approaches to harm reduction that focus on social activism and changing the underlying structural harms surrounding substance use, and the depoliticized institutional uses of harm reduction that focus on pragmatically managing individual substance use behaviour. In response to this tension and lack of theoretical clarity, the current paper proposes a critical pragmatist theoretical framework for harm reduction. First, I outline the basic principles of critical theory and pragmatism, and situate various harm reduction movements within these two theoretical foundations. Second, I explain how institutionalization has contributed to pragmatism being positioned as the primary underlying theory of harm reduction and the resulting limitations of this theoretical framing. Finally, I propose a theoretical framework that explicitly integrates both pragmatism and critical theory to help resist this process of institutionalization. I describe the key characteristics of critical pragmatism and outline the epistemological considerations of this pluralist approach as a theoretical framework for harm reduction. To conclude, I reflect on some of the challenges for harm reduction within a shifting political landscape 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 | 0.137 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.179 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.011 | 0.026 |
| Insufficient payload (model declined to judge) | 0.002 | 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".