The atlas network: a “strategic ally” of the tobacco industry
Bibliographic record
Abstract
Amid growing academic and policy interest in the influence of think tanks in public policy processes, this article demonstrates the extent of tobacco industry partnerships with think tanks in the USA, and analyzes how collaborating with a network of think tanks facilitated tobacco industry influence in public health policy. Through analysis of documents from tobacco companies and think tanks, we demonstrate that the Atlas Economic Research Foundation, a network of 449 free market think tanks, acted as a strategic ally to the tobacco industry throughout the 1990s. Atlas headquarters, while receiving donations from the industry, also channeled funding from tobacco corporations to think tank actors to produce publications supportive of industry positions. Thirty-seven per cent of Atlas partner think tanks in the USA received funding from the tobacco industry; the majority of which were also listed as collaborators on public relations strategies or as allies in countering tobacco control efforts. By funding multiple think tanks, within a shared network, the industry was able to generate a conversation among independent policy experts, which reflected its position in tobacco control debates. This demonstrates a coherent strategy by the tobacco industry to work with Atlas to influence public health policies from multiple directions. There is a need for critical analysis of the influence of think tanks in tobacco control and other health policy sectors, as well as greater transparency of their funding and other links to vested interests. © 2016 The Authors The International Journal of Health Planning and Management Published by John Wiley & Sons Ltd.
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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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".