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Record W2100615496 · doi:10.1136/jech.55.8.588

Tobacco industry efforts at discrediting scientific knowledge of environmental tobacco smoke: a review of internal industry documents

2001· review· en· W2100615496 on OpenAlexaboutno aff
Jeffrey Drope

Bibliographic record

VenueJournal of Epidemiology & Community Health · 2001
Typereview
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsTobacco industryCredibilityPublicationMedicinePosition (finance)Tobacco controlPublic relationsPublic healthEnvironmental healthBusinessPolitical scienceLawAdvertising

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.011
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.196
GPT teacher head0.475
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations121
Published2001
Admission routes1
Has abstractyes

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