Member states of the FCTC can generate self-sustaining funding by applying the polluter-pay principle to the tobacco industry
Bibliographic record
Abstract
Background and challenges to implementation Compared to the size of the problem and the public health work to be done, the FCTC remains woefully underfunded. Past attempts to raise funds, whether through assessed contributions, voluntary contributions, Overseas Development Assistance or other means have not generated funds commensurate with the size of the problem. Intervention or response The FCTC contains mechanisms for establishing subisidiary bodies. One such body could be established to receive funds from a new levy on tobacco revenue. For most FCTC member states this levy would be on money about to leave the country, destined for the head offices of multinational tobacco companies. Member states could agree to send a portion of the money so raised to WHO for global tobacco control programming. The rest could be used for national and regional tobacco control work. Results and lessons learnt A 5% levy, administered globally, would yield about USD one billion per year. If USD 200 million were designated for global tobacco control, Member States would collectively still have USD 800 million to spend on national and regional tobacco control programming. Conclusions and key recommendations At the next meeting of the Conference of the Parties, FCTC Member States should agree on a new globally coordinated tobacco levy, to be imposed by each Member State. At least USD 200 million should be designated for global tobacco control programming, to be administered by WHO, with the remainder of the proceeds from the levy to be spent on national and regional tobacco control programming.
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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 | 0.033 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.020 | 0.013 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".