Bibliographic record
Abstract
The tobacco industry has a long and well-documented history of influencing, exploiting and misleading public health and research communities.1–4 Starting with the 1953 ‘Tobacco Industry Research Committee’5, stakeholders affiliated with the tobacco industry have strategically promoted industry interests through the funding of research programmes and public health initiatives, in order to influence research agendas, manipulate the design, methods and conduct of research, affect interpretation of findings and selectively disseminate information through publications, conferences, forums and panels.1–4 These activities have enabled the tobacco industry to promote its versions of ‘sound science’ and ‘good epidemiology’ which have been designed to weaken consensus about the harms of tobacco use and second-hand smoke exposure.6 Academics and public health communities have sought to raise awareness about and challenge industry interference and manipulation,7–9 and some industry-funded research organisations such as the Council for Tobacco Research and the Center for Indoor Air Research were disbanded as part of the 1998 Master Settlement Agreement in the USA due to their extensively documented role in industry efforts to defraud the public.7 8 However, as Legg et al 10 highlight, the approach has intensified and become more sophisticated over time. A central tenet of the World Health Organization’s Framework Convention on Tobacco Control (FCTC),11 Article 5.3, states: ‘In setting and implementing their public health policies with respect to tobacco control, Parties shall act to protect these policies from commercial and other vested interests of the tobacco industry in accordance with national law’.11 There is now increasing evidence on the ways in which tobacco companies seek to position the industry as a strategic partner to public health, presenting themselves as scientific authorities who are promoting new products as solutions to concerns about commercial tobacco and as legitimate commentators on science and health …
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.069 | 0.023 |
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".