Analytical study on street smoking ban by local ordinances in Japan, 2002-2018
Bibliographic record
Abstract
Objective Since 2002, street smoking ban has been enforced by local ordinances in municipalities in Japan. We analyzed the trends and development of these ordinances came into force from 2002 to 2018. Methods The ordinances which include the word of “street smoking” or “smoke while walking” in the text were picked up in a national ordinance database. The text of each ordinance and the implementing regulations were downloaded for analysis. Results As of December 31st, 2018, 189 municipalities are identified to have enforced street smoking ban ordinances. 96 municipalities are found in KANTO area (around Tokyo) followed by 40 municipalities in KANSAI area (around Osaka) and their distribution is uneven. In the titles of the ordinances, the phrase; “littering ban” is used in 34 cases, the word; “environment” is used in 27 cases. In the text of the ordinances, “cigarette butts littering ban” is described in 122 cases, “secondhand smoking” is in 27 cases and “health” is found in only 12 ordinances. 107 municipalities provide penal regulations. The total number of municipalities has been increasing but after the 29 municipalities in 2008, the numbers of the new municipalities in each year is decreasing. Conclusions The main purposes of many ordinances are the cleanness of the city. Due to the rapid increase of inbound visitors, the enforcement of street smoking ban ordinance is expected to be increase. At the same time, indoor smoking ban which is not enough regulated at present supposed to be developed towards the Tokyo Olympic year, 2020.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".