Tobacco policy (in)coherence in Mozambique: an examination of national and subnational stakeholder perspectives
Bibliographic record
Abstract
Mozambique ranks fifth on the list of tobacco producing countries in Africa, while also being a Party to the WHO Framework Convention on Tobacco Control (FCTC). Tobacco farming is regarded by some governments as a strategic economic commodity for export and remains deeply entrenched within Mozambique's political and economic landscape. This study uses a qualitative description methodology to identify tensions, conflicts and alignment or misalignment in policy on tobacco across government sectors and levels in Mozambique. We conducted semi-structured qualitative interviews with 33 key informants from sectors across national and subnational levels including health, agriculture, economic and commercial sectors, as well as non-state actors from civil society organizations, the tobacco industry, farmers unions and associations and individual farmers. Incoherence was present across sectoral mandates, perspectives on industry's presence in the country and regions and between FCTC provisions and informant perceptions of tobacco production as a development strategy. Despite tobacco being viewed as an important economic commodity by many informants, there was also widespread dissatisfaction with tobacco from both farmers and some government officials. There were indications of an openness to shifting to a policy that emphasizes alternatives to tobacco growing. The findings also illustrate where points of convergence exist across sectors and where opportunities for aligning tobacco policy with the provisions of the FCTC can occur.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".