Follow the money: How the billions of dollars that flow from smokers in poor nations to companies in rich nations greatly exceed funding for global tobacco control and what might be done about it
Bibliographic record
Abstract
The business of selling cigarettes is increasingly concentrated in the hands of five tobacco companies that collectively control almost 90% of the world's cigarette market, four of which are publicly traded corporations. The economic activities of these cigarette manufacturers can be monitored through their reports to shareholders and other public documents. Reports for 2008 show that the revenues of these five companies exceeded $300 billion, of which more than $160 billion was provided to governments as taxes, and that corporate earnings of the four publicly traded companies were over $25 billion, of which $14 billion was retained after corporate income taxes were paid. By contrast, funding for domestic and international tobacco control is not reliably reported. Estimated funding for global tobacco control in 2008, at $240 million, is significantly lower than resources provided to address other highmortality global health challenges. Tobacco control has not yet benefited from the innovative finance mechanisms that are in place for HIV/AIDS, tuberculosis and malaria. The Framework Convention On Tobacco Control (FCTC) process could be used to redirect some of the earnings from transnational tobacco sales to fund FCTC implementation or other global health efforts.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".