Smoke Rings: Towards a Comprehensive Tobacco Free Policy for the Olympic Games
Bibliographic record
Abstract
BACKGROUND: The tobacco industry has long sought affiliation with major sporting events, including the Olympic Games, for marketing, advertising and promotion purposes. Since 1988, each Olympic Games has adopted a tobacco-free policy. Limited study of the effectiveness of the smoke-free policy has been undertaken to date, with none examining the tobacco industry's involvement with the Olympics or use of the Olympic brand. METHODS AND FINDINGS: A comparison of the contents of Olympic tobacco-free policies from 1988 to 2014 was carried out by searching the websites of the IOC and host NOCs. The specific tobacco control measures adopted for each Games were compiled and compared with measures recommended by the WHO Tobacco Free Sports Initiative and Article 13 of the Framework Convention on Tobacco Control (FCTC). This was supported by semi-structured interviews of key informants involved with the adoption of tobacco-free policies for selected games. To understand the industry's interests in the Olympics, the Legacy Tobacco Documents Library (http://legacy.library.ucsf.edu) was systematically searched between June 2013 and August 2014. Company websites, secondary sources and media reports were also searched to triangulate the above data sources. This paper finds that, while most direct associations between tobacco and the Olympics have been prohibited since 1988, a variety of indirect associations undermine the Olympic tobacco-free policy. This is due to variation in the scope of tobacco-free policies, limited jurisdiction and continued efforts by the industry to be associated with Olympic ideals. CONCLUSIONS: The paper concludes that, compatible with the IOC's commitment to promoting healthy lifestyles, a comprehensive tobacco-free policy with standardized and binding measures should be adopted by the International Olympic Committee and all national Olympic committees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".