Surveying Alcohol Outlet Density in Four Neighborhoods of Beirut Lebanon: Implications for Future Research and National Policy
Bibliographic record
Abstract
Underage drinking among youth in Lebanon is increasing. Regulating availability is one of the best buy policies recommended by the World Health Organization. To quantitatively document the current status of alcohol availability to youth in Lebanon, we used GPS technology to survey alcohol outlet density in four highly populated neighborhoods in Beirut, Lebanon, and to estimate their proximity to educational institutions. The density of alcohol outlets ranged from 18.30 to 80.95 per km² (average of 39.6 alcohol outlets/km²). The highest number of total alcohol outlets was in the "Hamra & Jamiaa" area, which includes one of the largest private universities in the country. Thirteen out of 109 (12%) alcohol outlets (on and off-premise) were located less 100 m away from educational institutions, in violation of the current licensing law. None of the off-premise and the majority (94%) of on-premise alcohol outlets displayed the "no sale for <18" sign. Findings were indicative of an environment conducive to increased access and availability of alcohol among youth in Lebanon probably attributed to the prevailing weak alcohol policies and their enforcement. Systematic collection and reporting of alcohol outlet densities is critical to understand the alcogenic environment and guide local harm reduction policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".