Noticed and then Forgotten: Gender in Alcohol Policy Stakeholder Responses to Alcohol and Violence
Bibliographic record
Abstract
In this article, we analyse interview data on how alcohol policy stakeholders in Australia, Canada and Sweden understand the relationship between men, masculinities, alcohol and violence. Using influential feminist scholarship on public policy and liberal political theory to analyse interviews with 42 alcohol policy stakeholders, we argue that while these stakeholders view men's violence as a key issue for intervention, masculinities are backgrounded in proposed responses and men positioned as unamenable to intervention. Instead, policy stakeholders prioritise generic interventions understood to protect all from the harms of men's drinking and violence without marking men for special attention. Shared across the data is a prioritisation of interventions that focus on harms recognised as relating to men's drinking but apply equally to all people and, as such, avoid naming men and masculinities as central to alcohol-related violence. We argue that this process works to background the role of masculinities in violence, leaving men unmarked and many possible targeted responses unthinkable.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.032 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".