Public perceptions and alcohol policies: Six case studies that examine trends and interactions
Bibliographic record
Abstract
Public perceptions and alcohol policies: Six case studies that examine trends and interactionsThere are several factors other than public opinion that contribute to the selection, implementation and modification of alcohol policies.These include the desire by governments to generate revenue or reduce slippage of sales to adjacent jurisdictions; pressures from vested interests, such as alcohol producers or retailers, to streamline regulations or increase access to alcohol; public health and safety advocacy (e.g.campaigns to control drinking and driving); and deregulation, such as privatising alcohol retailing, driven by ideological perspectives.Their relative and combined impact is not well charted, and neither are their interactions with public opinion.Proponents both of greater access and of controls on availability may claim that public opinion is on their side.Public opinion on alcohol policy issues may be a force of secondary potency in comparison with the policy vectors noted above.However, as these six papers illustrate, when considered together, there is much interaction between alcohol policies and public opinion.Furthermore, in a few cases reported here, three dimensions seem to be interrelated: apparent awareness of alcohol-related harm or disruptions, public opinion on alcohol policies, and modifications in alcohol policies.While the methodological resources typically do not allow for firm causal interpretations, the findings are sufficiently provocative to stimulate future work to examine these concurrent trends.The Australian paper by Sarah Callinan and co-authors [1] assesses attitudes on alcohol policy between 1995 and 2010.The authors note that there was a turning point in 2004, with decreasing support for alcohol control policies before then and increasing support for alcohol policy restrictions after 2004.This shift was evident across all age groups and not limited to one demographic sector.The authors speculate that while no single policy initiative appears to have stimulated this turn-around in support for control policies, the increasingly liberal licencing arrangements in many Australian states, including the expansion in the number and type of outlets, may have sparked concern among respondents.The paper based on Ontario, Canada, by Anca Ialomiteanu and colleagues [2], investigates public
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".