Tobacco farming: overcoming an understated impediment to comprehensive tobacco control
Bibliographic record
Abstract
Tobacco farming has emerged as an important concern for tobacco control advocates. Tobacco-growing countries face unique and important challenges to comprehensive, intersectoral tobacco control. These challenges stem from narratives that position tobacco as an important driver of economic growth and development, perpetuated by tobacco interests with close ties to government decision-making. While the global tobacco control movement has enshrined a commitment to alternatives to tobacco growing, there remain numerous obstacles. Tobacco growing is often situated in contexts with limited markets for other agricultural products, limited knowledge and economic resources to pursue alternatives, and/or a structure that favours industry control over the supply chain, all constraining the decision space of farmers. An evidence-informed approach is necessary to address tobacco supply, including growing, processing, manufacturing and trade, in this complex context. This paper reviews the economic, environmental and policy context of tobacco growing with an emphasis on the past decade of empirical work on the political economy of tobacco supply and introduces strategies to pursue alternatives. This analysis debunks many of the arguments used to perpetuate the narrative of tobacco's prosperity and provides critical insights into the institutional constraints faced by government sectors in pursuing a policy of alternatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".