Classifying alcohol control policies enacted between 2000 and 2020 in Poland and the Baltic countries to model potential impact
Bibliographic record
Abstract
AIMS: The study's aim is to identify and classify the most important alcohol control policies in the Baltic countries (Estonia, Latvia and Lithuania) and Poland between 2000 and 2020. METHODS: Policy analysis of Baltic countries and Poland, predicting potential policy impact on alcohol consumption, all-cause mortality and alcohol-attributable hospitalizations was discussed. RESULTS: All Baltic countries implemented stringent availability restrictions on off-premises trading hours and different degrees of taxation increases to reduce the affordability of alcoholic beverages, as well as various degrees of bans on alcohol marketing. In contrast, Poland implemented few excise taxation increases or availability restrictions and, in fact, reduced stipulations on prior marketing bans. CONCLUSIONS: This classification of alcohol control policies in the Baltic countries and Poland provides a basis for future modeling of the impact of implementing effective alcohol control policies (Baltic countries), as well as the effects of loosening such policies (Poland).
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".