An Overview of the Policy and Market Landscape of Tobacco Production and Control in Mozambique
Bibliographic record
Abstract
Background: Tobacco growing has been considered a mainstay of Mozambique’s economy, but there is a dearth of analysis of the tobacco policy landscape in the country. Methods: Review of government and non-government documents and academic papers addressing Mozambique’s tobacco-growing history, the changes in the political economy of tobacco, and health policies addressing tobacco use and prevention of noncommunicable diseases. Results: Despite its tobacco growing and exporting history, the contribution of tobacco to the economy has been in steady decline in the past two decades, including in the areas dedicated to growing. At the same time there has been an increase in multinational control of the tobacco economy. In parallel, Mozambique’s commitment to addressing the growing burden of noncommunicable disease and accession to the Framework Convention on Tobacco Control indicate a potential for internal government tensions to balance immediate economic interests with long term health goals. Conclusions: With the decline in tobacco share of the overall economy, Mozambique may be well-positioned to explore alternative, sustainable livelihoods for farmers that grow tobacco, but it must overcome inter-sectoral barriers and advocate for a whole of government approach to address the health and economic impact of tobacco.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".