Do smoke-free laws affect revenues in pubs and restaurants?
Bibliographic record
Abstract
In the debate about laws regulating smoking in restaurants and pubs, there has been some controversy as to whether smoke-free laws would reduce revenues in the hospitality industry. Norway presents an interesting case for three reasons. First, it was among the first countries to implement smoke-free laws, so it is possible to assess the long-term effects. Second, it has a cold climate so if there is a negative effect on revenue one would expect to find it in Norway. Third, the data from Norway are detailed enough to distinguish between revenue from pubs and restaurants. Autoregressive integrated moving average (ARIMA) intervention analysis of bi-monthly observations of revenues in restaurants and pubs show that the law did not have a statistically significant long-term effect on revenue in restaurants or on restaurant revenue as a share of personal consumption. Similar analysis for pubs shows that there was no significant long-run effect on pub revenue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".