Doing it by numbers: A simple approach to reducing the harms of alcohol
Bibliographic record
Abstract
Alcohol use is one of the top five causes of disease and disability in almost all countries in Europe, and in the eastern part of Europe it is the number one cause. In the UK, alcohol is now the leading cause of death in men between the ages of 16-54 years, accounting for over 20% of the total. Europeans above 15 years of age in the EU on average consume alcohol at a level which is twice as high as the world average. Alcohol should therefore be a public health priority, but it is not. This paper puts forward a new approach to reduce alcohol use and harms that would have major public health and social impacts. Our approach comprises individual behaviour and policy elements. It is based on the assumption that heavy drinking is key. It is simple, so it would be easy to introduce, and because it lacks stigmatising issues such as the diagnosis of addiction and dependence, it should not be contentious.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".