Tobacco industry efforts at discrediting scientific knowledge of environmental tobacco smoke: a review of internal industry documents
Bibliographic record
Abstract
STUDY OBJECTIVE: Using tobacco industry internal documents to investigate the use of tobacco industry consulting scientists to discredit scientific knowledge of environmental tobacco smoke (ETS). DESIGN: Basic and advanced searches were performed on the Philip Morris, Tobacco Institute, R J Reynolds, Brown and Williamson, Lorillard, and the Council for Tobacco Research document web sites, with a concentration on the years 1985-1995. Guildford depository files located on the Canadian Council on Tobacco Control website were also searched. The documents were found in searches undertaken between 1 March and 30 June 2000. MAIN RESULTS: The industry built up networks of scientists sympathetic to its position that ETS is an insignificant health risk. Industry lawyers had a large role in determining what science would be pursued. The industry funded independent organisations to produce research that appeared separate from the industry and would boost its credibility. Industry organised symposiums were used to publish non-peer reviewed research. Unfavourable research conducted or proposed by industry scientists was prevented from becoming public. CONCLUSIONS: Industry documents illustrate a deliberate strategy to use scientific consultants to discredit the science on ETS.
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 | 0.037 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".