Legal Ages for Purchase and Consumption of Alcohol and Heavy Drinking among College Students in Canada, Europe, and the United States
Bibliographic record
Abstract
Heavy drinking and associated negative consequences remain a serious problem among college students. In a secondary analysis of data from two published study, the authors examine the correlation between minimum legal age to purchase and/or consume alcohol and rates of heavy drinking among college students in 22 countries. The published studies use identical definitions of heavy drinking and similar methodologies. In the study of 20 European countries and the United States, there is a positive correlation between prevalence of heavy drinking and both minimum legal purchase age (r =.34) and minimum legal drinking age (r =.19); in the study of Canada and the United States, there is a perfect positive correlation (r = 1.0). Examination of this evidence does not support the conclusion that a lower minimum legal age for purchase and/or consumption of alcoholic beverages is a protective factor for decreasing heavy drinking among college students.
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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".