Alcohol: No Ordinary Commodity
Bibliographic record
Abstract
Abstract This book is about alcohol policy: why it is needed, how it is made, and the impact it has on health and well-being. It is written for both policymakers and alcohol scientists, as well as the many other people interested in bridging the gap between research and policy. It begins with a global review of epidemiological evidence showing why alcohol is not an ordinary commodity, and it ends with the conclusion that alcohol policies implemented within a public health agenda are needed to reduce the enormous burden of harm it causes. The core of the book is a critical review of the cumulative scientific evidence in seven general areas of alcohol policy: pricing and taxation; regulating the physical availability of alcohol; modifying the environment in which drinking occurs; drink-driving countermeasures; marketing restrictions; primary prevention programmes in schools and other settings; and treatment and early intervention services. The final chapters discuss the current state of alcohol policy in different parts of the world, the detrimental role of the alcohol industry, and the need for both national and global alcohol policies that are evidence-based, effective, and coordinated. This book shows that opportunities for 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.071 | 0.039 |
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".