<i>Alcohol: No Ordinary Commodity</i>—a summary of the third edition
Bibliographic record
Abstract
BACKGROUND AND AIMS: This article summarizes the findings and conclusions of the third edition of Alcohol: No Ordinary Commodity. The latest revision of this book is part of a series of monographs designed to provide a critical review of the scientific evidence related to alcohol control policy from a public health perspective. DESIGN: A narrative summary of the contents of the book according to five major issues. FINDINGS: An extensive amount of epidemiological evidence shows that alcohol is a major contributor to the global burden of disease, disability and death in high-, middle- and low-income countries. Trends in alcohol products and marketing are described, indicating that a large part of the global industry has been consolidated into a small number of transnational corporations that are expanding their operations in Asia, Africa and Latin America. The main part of the book is devoted to a review of strategies and interventions designed to prevent or minimize alcohol-related harm. Overall, the most effective strategies to protect public health are taxation that decreases affordability and restrictions on the physical availability of alcohol. A total ban on alcohol marketing is also an effective strategy to reduce consumption. In addition, drink-driving counter-measures, brief interventions with at-risk drinkers and treatment of drinkers with alcohol dependence are effective in preventing harm in high-risk contexts and groups of hazardous drinkers. CONCLUSION: Alcohol policy is often the product of competing interests, values and ideologies, with the evidence suggesting that the conflicting interests between profit and health mean that working in partnership with the alcohol industry is likely to lead to ineffective policy. Opportunities for implementation of evidence-based alcohol policies that better serve the public good are clearer than ever before as a result of accumulating knowledge on which strategies work best.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.022 |
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".