Criminal Law and the Counter-Hegemonic Potential of\nHarm Reduction
Bibliographic record
Abstract
Harm reduction approaches to drug use have been lauded for saving lives, being cost-effective, elevating pragmatism over prohibitionist ideology, being flexible in tailoring responses to the problem, and for their counter-hegemonic potential to empower people who use drugs. This article examines the legal systems engagement with harm reduction, and, in particular,recent cases that incorporate harm reduction s focus on empirical evidence in policy making into Canadian constitutional rights jurisprudence. It argues that harm reduction approaches in this venue may hold promise as a bulwark against some of the marginalizing features of traditional criminaljustice approaches. However, the article also warns of a risk of inadvertently reinforcing the dominant discourse of criminalization and stigmatization as harm reduction s features are embodied within the institutional frameworks of the state.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.092 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 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".