Engaging youth in alcohol policy: The Lee Law Project
Bibliographic record
Abstract
Mosher, J., & D’Andrea, M. (2015). Engaging youth in alcohol policy: The Lee Law Project. The International Journal Of Alcohol And Drug Research, 4(2), 113-118. doi:http://dx.doi.org/10.7895/ijadr.v4i2.206Aims: (1) Conduct a pilot project to test the effectiveness of a youth development “toolkit” designed to reduce youth exposure tosignage on liquor store windows; (2) Highlight the disparity in violation rates of a state law limiting window signage on liquorstore windows between low income communities of color and higher income, predominantly Anglo communities.Design: Pilot project/case study. Participating young people, working with adult coaches, photographed liquor store windows inthree communities and determined level of compliance with state law limiting liquor store window signage to 33 percent of totalwindow area and requiring clear view of cash register area in the store.Setting: Three communities in Santa Cruz County, California, with diverse income and racial/ethnic compositions.Participants: 71 liquor stores.Measures: Compliance rates of participating liquor stores with state law limiting the amount and placement of window signage.Findings: Low income, predominantly Latino community had significantly lower compliance rates than two nearby higherincome, Anglo communities. Youth participants successfully engaged community organizations and policy makers in advocatingfor voluntary compliance.Conclusions: The toolkit provides a promising model for engaging youth in alcohol policy reform and reducing youth exposureto liquor store signage.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".