The marketing of responsible drinking: Competing voices and interests
Bibliographic record
Abstract
INTRODUCTION AND AIMS: This paper contrasts health-oriented low-risk drinking guidelines (LRDGs) with social drinking marketing and popular advice on the amount of alcohol to be provided for social occasions. The questions addressed include:What is the underlying evidence base and rationale for health-oriented versus socially oriented drinking guidelines?What are the recommended amounts of alcohol per person from the LRDGs and from popular advice? DESIGN AND METHODS: This paper draws on existing research, archival data, websites, print media and key informant interviews. The focus is on recent information on LRDGs and social drinking indicators in Canada, the USA, Australia and the UK. RESULTS: There is extensive epidemiological research indicating the associations between drinking pattern and risk for chronic disease and trauma as well as certain potential health benefits from drinking small amounts regularly. This body of evidence is one resource for government or medically sanctioned LRDGs in many jurisdictions. In contrast, for those planning social events where liquor is served, information is available from the hospitality industry, retailers and liquor control boards.While some overlap exists between these two sources of information, in some contexts normative recommendations support drinking at potentially dangerous levels. DISCUSSION AND CONCLUSIONS: The inconsistency among the different guidelines highlights one of the challenges of conveying health information on a drug that is integrated into social life and used extensively. It also reflects a siloed approach to alcohol policy—where retailing and harm reduction practices are managed by different sectors of government that seldom reflect a coordinated response.
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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 | 0.045 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.021 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.025 | 0.015 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".