Comparative perspectives and development planning : the anti-smoking legislation in Guangzhou
Bibliographic record
Abstract
The large proportion of smokers in China and the social consequences have had a damaging impact on public health as well as on the society. As a sub-national legislation, the Guangzhou anti-smoking legislations in Guangzhou play an important role in the legislative control in China since it is one of the most severe local tobacco control laws in China. Many legislative experts, public health professionals and even decision makers have great hope on this law. But the consequence of this law is disappointed. And this anti-smoking legislation has exposed a lot of problems. \n \nThis article introduces the current status of tobacco control legislation in Guangzhou; analyzes the effectiveness of Hong Kong and Canada's tobacco control law; identifies four areas (the weak effect of the anti-smoking law; inappropriate penalties; limited governmental capacity and uncertain political will and the lack of awareness) in the anti-smoking legislation in Guangzhou that are problematic. Finally, this project discusses what can we learn from other countries' legislative experiences, including making clear definitions of key terms in anti-smoking laws; change the way of penalty; increasing the regulation of tobacco packaging and increasing the governmental capacity, which aims at proposing some legislative options for a much more effective tobacco control movement in Guangzhou in the future.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".