Bibliographic record
Abstract
The tobacco industry first began to promote the idea that a minority of smokers are 'genetically predisposed' to lung cancer in the 1950s. We used tobacco industry documents available as a result of litigation to investigate the role of the tobacco industry in funding the 'scientific bandwagon' described by Fujimura, in which genetics has come to dominate the cancer research agenda. From 1990-1995 inclusive, 52% of the project funding allocated by British American Tobacco's Scientific Research Group went to genetic research, mainly based in universities and at one cancer charity. The largest project involved a pharmacogenetic research unit, based in a UK medical school, which was established with the help of tobacco industry PR consultants in 1988. The unit received half its project funding from the industry in 1992. Its main aim was to identify a minority of smokers who are supposedly 'genetically susceptible' to lung cancer, so that smoking cessation measures could be targeted at them. This aim was adopted and promoted by influential scientists at the US National Institutes of Health and the UK Medical Research Council in the late 1980s, in the run up to the Human Genome Project. BAT's research funds were also used to counter claims by others to have identified a unique 'genetic fingerprint' for tobacco smoke in lung cancer cells. We conclude that the tobacco industry has played a significant role in shaping research agendas, in particular, by promoting the idea that individual genome screening would be of benefit to public 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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".