Pricing as a means of controlling alcohol consumption
Bibliographic record
Abstract
Background: Reducing the affordability of alcohol, by increasing its price, is the most effective strategy for controlling alcohol consumption and reducing harm. Sources of data: We review meta-analyses and systematic reviews of alcohol tax/price effects from the past decade, and recent evaluations of tax/price policies in the UK, Canada and Australia. Areas of agreement: While the magnitudes of price effects vary by sub-group and alcoholic beverage type, it has been consistently shown that price increases lead to reductions in alcohol consumption. Areas of controversy: There remains, however, a lack of consensus on the most appropriate taxation and pricing policy in many countries because of concerns about effects by different consumption level and income level and disagreement on policy design between parts of the alcoholic beverage industries. Growing points: Recent developments in the research highlight the importance of obtaining accurate alcohol price data, reducing bias in estimating price responsiveness, and examining the impact on the heaviest drinkers. Areas timely for developing research: There is a need for further research focusing on the substitution effects of taxation and pricing policies, estimation of the true tax pass-through rates, and empirical analysis of the supply-side response (from alcohol producers and retailers) to various alcohol pricing strategies.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".