Patterns of alcohol policy enforcement activities among local law enforcement agencies: A latent class analysis
Bibliographic record
Abstract
AIMS: We assessed levels and patterns of alcohol policy enforcement activities among U.S. local law enforcement agencies. DESIGN/SETTING/PARTICIPANTS: We conducted a cross-sectional survey of a representative sample of 1,631 local law enforcement agencies across the 50 states. MEASURES/METHODS: We assessed 29 alcohol policy enforcement activities within each of five enforcement domains-underage alcohol possession/consumption, underage alcohol provision, underage alcohol sales, impaired driving, and overservice of alcohol-and conducted a series of latent class analyses to identify unique classes or patterns of enforcement activity for each domain. FINDINGS: We identified three to four unique enforcement activity classes for each of the enforcement domains. In four of the domains, we identified a Uniformly Low class (i.e., little or no enforcement) and a Uniformly High enforcement activity class (i.e., relatively high levels of enforcement), with one or two middle classes where some but not all activities were conducted. The underage provision domain had a Uniformly Low class but not a Uniformly High class. The Uniformly Low class was the most prevalent class in three domains: underage provision (58%), underage sales (61%), and overservice (79%). In contrast, less than a quarter of agencies were in Uniformly High classes. CONCLUSIONS: We identified qualitatively distinct patterns of enforcement activity, with a large proportion of agencies in classes characterized by little or no enforcement and fewer agencies in high enforcement classes. An important next step is to determine if these patterns are associated with rates of alcohol use and alcohol-related injury and mortality.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".