Economic effect of a smoke-free law in a tobacco-growing community: Table 1
Bibliographic record
Abstract
OBJECTIVE: To determine whether Lexington, Kentucky's smoke-free law affected employment and business closures in restaurants and bars. On 27 April 2004, Lexington-Fayette County implemented a comprehensive ordinance prohibiting smoking in all public buildings, including bars and restaurants. Lexington is located in a major tobacco-growing state that has the highest smoking rate in the US and was the first Kentucky community to become smoke-free. DESIGN: A fixed-effects time series design to estimate the effect of the smoke-free law on employment and ordinary least squares to estimate the effect on business openings and closings. SUBJECTS AND SETTINGS: All restaurants and bars in Lexington-Fayette County, Kentucky and the six contiguous counties. MAIN OUTCOME MEASURES: ES-202 employment data from the Kentucky Workforce Cabinet; Business opening/closings data from the Lexington-Fayette County Health Department, Environmental Division. RESULTS: A positive and significant relationship was observed between the smoke-free legislation and restaurant employment, but no significant relationship was observed with bar employment. No relationship was observed between the law's implementation and employment in contiguous counties nor between the smoke-free law and business openings or closures in alcohol-serving and or non-alcohol-serving businesses. CONCLUSIONS: No important economic harm stemmed from the smoke-free legislation over the period studied, despite the fact that Lexington is located in a tobacco-producing state with higher-than-average smoking rates.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".