Minimum Purchase Age Laws: How Effective Are They in Reducing Alcohol-Impaired Driving?
Bibliographic record
Abstract
Young drivers are less likely than adults to drive after alcohol, but their crash risk is substantially higher when they do. This is especially true at low and moderate blood alcohol concentrations (BACs) and is thought to result from teenagers? relative inexperience with drinking, driving, and combining the two. Since July 1988, all 50 U.S. states and Washington, D.C., have had laws that require people to be at least 21 years old to purchase alcohol. Many other countries, however, allow people younger than 21 to drink alcohol. Minimum legal ages are 16 to 18 in most European countries, 18 to 19 in Canada, 18 in Australia, and 20 in New Zealand. Laws that establish a to drink alcohol are the primary legal mechanism limiting teenagers' access to alcohol. In the United States, zero tolerance laws that make it illegal for people younger than 21 to drive with any measurable amount of alcohol in their bodies, and legal (MLDA) laws of 21 are the primary legal countermeasures against underage and driving. This paper summarizes trends in alcohol-impaired driving among people younger than 21, the history of legal alcohol laws, and the evidence of their effects. Laws vary with regard to whether they prohibit the purchase, consumption, or possession of alcohol by underage people (here referring to those 20 and younger). For simplicity, the terms drinking age and minimum legal age, collectively abbreviated as MLDA, are used to refer to all of these types of laws. The paper focuses primarily on the United States, where the bulk of research has been conducted.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".