Emergent publics of alcohol and other drug policymaking
Bibliographic record
Abstract
Alcohol and other drug (AOD) policy is developed within complex networks of social, economic, and political forces. One of the key ideas informing this development is that of the ‘public’ of AOD problems and policy solutions. To date, however, little scholarly attention has been paid to notions of the public in AOD policymaking. Precisely how are publics articulated by those tasked with policy development and implementation? In this article, we explore this question in detail. We analyze 60 qualitative interviews with Australian and Canadian AOD policymakers and service providers, arguing that publics figure in these interviews as pre-existing groups that must be managed – contained or educated – to allow policy to proceed. Drawing on Michael Warner’s work, we argue that publics should be understood instead as made in policy processes rather than as preceding them, and we conclude by reframing publics as emergent collectivities of interest. In closing, we briefly scrutinize the widely accepted model of good policy development, that of ‘consultation,’ arguing that, if publics are to be understood as emergent, and therefore policy’s opportunities as more open than is often suggested, a different figure – here that of ‘conference’ is tentatively suggested – may be required.
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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 | 0.030 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.039 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".