Research agendas for alcohol policymaking in the wider world
Bibliographic record
Abstract
From comparisons of World Health Organization statistics, it is clear that people in lower-income countries experience more harms per litre of alcohol and different types of harms compared to those from higher-income countries. Yet studies in higher-income countries dominate research on policies to prevent alcohol problems. The paper reports on results of collaborative work to map priority areas for research relevant to low- and middle-income countries. Research focus areas were identified and discussed among potential coauthors from diverse fields with relevant knowledge, with agreement reached on an initial list of seven research priority areas. Areas identified include: (1) the effects of choices (e.g., national vs. local, monopoly vs. licensing system) in organising the alcohol market; (2) involvement/separation of alcohol industry interests in decisions on public health regulation; (3) options and effectiveness of global agreements on alcohol governance; (4) choices and experience in controlling unrecorded alcohol; (5) means of decreasing harm from men’s drinking to family members; (6) strategies for reducing the effects of poverty on drinking’s role in harms; and (7) measuring and addressing key alcohol-induced low-and middle-income country (LMIC) health harms: infectious diseases, injuries, digestive diseases. Paths ahead for such research are briefly outlined.
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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 | 0.077 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.034 | 0.036 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".