‘If you kill tobacco, you kill Malawi’: Structural barriers to tobacco diversification for sustainable development
Bibliographic record
Abstract
Abstract There is growing recognition that diversifying away from tobacco farming can contribute to progress towards the Sustainable Development Goals in lower‐ and middle‐income countries. However, diversification projects are often limited in scope and impact. This paper analyses structural barriers to tobacco diversification and opportunities to challenge them, based on the perceptions of those engaged in policy processes in Malawi, the most tobacco dependent country in the world. Through analysis of 11 key informant interviews, four structural barriers are identified: perceived economic importance, lack of alternatives, vested industry interests, and the polarised conflict between tobacco control advocates and farmers. Respondents suggest these might be overcome through import substitution, a focus on securing alternative markets, and the inclusion of tobacco farmers in global processes. In ratifying the Framework Convention on Tobacco Control, the Government of Malawi would be able to access diversification support, build a positive relationship with the global tobacco control community and address industry influence.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".