Pricing as a means of controlling alcohol consumption
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 it