Harm reduction: Philosophical drivers of conceptual tensions and ways forward
Bibliographic record
Abstract
Of the various debates surrounding harm reduction, a conceptual tension that perhaps has the most relevance for the provision of services is that of harm reduction as a technical solution versus a contextualized social practice. The aim of this paper was to examine this conceptual tension. First, the two perspectives will be presented through the use of examples. Second, philosophical drivers that serve to underpin and justify each perspective will be explicated at the level of the knowledge that we privilege; the ideologies that we subscribe to; and the interests that we stand to serve. In this paper, I argue that the existing tension between technical and social approaches to harm reduction is embedded within discord pertaining to ways of knowing, paradigms of inquiry, prevailing ideologies, and notions of harm and risk. Building on these sources of tension, I suggest a means of philosophical reconciliation between the two approaches and ways forward, namely through acknowledging multiple sources of knowledge, through embracing paradigmatic incommensurability, through considering alternative conceptions of people who use drugs as political subjects, through involving service providers and end-users in shared decision-making, and lastly through reaffirming people who use drugs as the intended beneficiaries of services.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.019 | 0.183 |
| Scholarly communication | 0.046 | 0.073 |
| Open science | 0.009 | 0.030 |
| Research integrity | 0.022 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 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".