Complying With the Minimum Drinking Age: Effects of Enforcement and Training Interventions
Bibliographic record
Abstract
This article summarizes the proceedings of a symposium presented at the 2004 Research Society on Alcoholism meeting in Vancouver, British Columbia, organized by Alexander C. Wagenaar and chaired by Mark S. Goldman. The purpose of the symposium was to present the design and outcomes from a recently completed multi-community controlled time-series trial entitled Complying with the Minimum Drinking Age (CMDA), which tested two approaches for enhancing the effectiveness of the legal drinking age policy: training of alcohol retailers, and police enforcement at alcohol establishments. Specific presentations were: (1) Introduction and Overview of the CMDA project by Alexander C. Wagenaar, (2) CMDA Interventions by Traci L. Toomey, (3) CMDA Measurement, Statistical Methods and Results by Darin J. Erickson, and (4) Conclusions, Implications and Future Applications by Alexander C. Wagenaar. Results from the trial show: (1) minimal effects of the brief version of Alcohol Risk Management training for alcohol outlet management, (2) significant effects of enforcement checks in reducing sales of alcohol to youth, (3) a concentration of effects in specific alcohol outlets experiencing an enforcement check with little diffusion of effects to other outlets in the community not experiencing a check, and (4) a substantial decay of enforcement effects over the three months following a specific check. In short, results showed significant and substantial specific deterrence effects, and little training effects. Results also illustrated the utility and strength of the controlled time-series trial research design. Additional research on temporal and geospatial distribution of community-level intervention effects is warranted.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".