Emergent publics of alcohol and other drug policymaking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it