Mind your "smoking manners": the tobacco industry tactics to normalize smoking in Japan.
Bibliographic record
Abstract
The tobacco industry has adapted its promotional strategies as tobacco-control measures have increased. This paper describes the tobacco industry's strategies on smoking manners and illustrates how these interfere with tobacco-control policy in Japan where tobacco control remains weak. Information on the tobacco industry's promotional strategies in Japan was collected through direct observation, a review of tobacco industry documents and a literature review. The limitation of the study would be a lack of industry documents from Japan as we relied on a database of a U.S. institution to collect internal documents from the tobacco industry. Japan Tobacco began using the manners strategies in the early 1960s. Collaborating with wide range of actors -including local governments and companies- the tobacco industry has promoted smoking manners to wider audiences through its advertising and corporate social responsibility activities. The tobacco industry in Japan has taken advantage of the cultural value placed on manners in Japan to increase the social acceptability of smoking, eventually aiming to diminish public support for smoke-free policies that threatens the industry's business. A stronger enforcement of the WHO Framework Convention on Tobacco Control is critical to counteracting such strategies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".