Bibliographic record
Abstract
Alcoholic beverages, and their harmful use, have been familiar fixtures in human societies since the beginning of recorded history. Worldwide, alcohol is a leading cause of ill health and premature mortality. It accounts for 1 in 17 deaths, and for a significant proportion of disabilities, especially in men. In OECD countries, alcohol consumption is about twice the world average. Its social costs are estimated in excess of 1% of GDP in high- and middle-income countries. When it is not the result of addiction, alcohol use is an individual choice, driven by social norms, with strong cultural connotations. This is reflected in unique patterns of social disparity in drinking, showing the well-to-do in some cases more prone to hazardous use of alcohol, and a polarisation of problem-drinking at the two ends of the social spectrum. Certain patterns of drinking have social impacts, which provide a strong economic rationale for governments to influence the use of alcohol through policies aimed at curbing harms, including those occurring to people other than drinkers. Some policy approaches are more effective and efficient than others, depending on their ability to trigger changes in social norms, and on how well they can target the groups that are most at risk. This book provides a detailed examination of trends and social disparities in alcohol consumption. It offers a wide-ranging assessment of the health, social and economic impacts of key policy options for tackling alcohol-related harms in three OECD countries (Canada, the Czech Republic and Germany), extracting relevant policy messages for a broader set of countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".