Into the ordinary: non-elite actors and the mobility of harm reduction policies
Bibliographic record
Abstract
Abstract Research on policy transfer and policy mobility has focused much attention on relatively elite actors, such as politicians, international organisations, think tanks, philanthropic donors, and consultancy firms. In contrast, this article uses the case of ‘harm reduction’ drug policy, an area of practice and research that is committed to valuing ‘non-elite’ actors, to show how they are frequently involved in mobilizing policy knowledge. Focusing on the role of service providers, activists and service users in the mobilization of harm reduction models, the paper discusses four key practices associated with these non-elite actors: cooperation, convergence, disobedience and display. The article argues that the deep involvement of relatively non-elite actors in mobilizing harm reduction policies means that multi-disciplinary scholarship would be enriched by going ‘into the ordinary’ in a wide range of policy contexts.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.056 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.001 | 0.023 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".