The Political Economy of Tobacco in Mozambique and Zimbabwe: A Triangulation Mixed Methods Protocol
Bibliographic record
Abstract
Changing global markets have generated a dramatic shift in tobacco consumption from high-income countries (HICs) to low- and middle-income countries (LMICs); by 2030, more than 80% of the disease burden from tobacco use will fall on LMICs. Propelling this shift, opponents of tobacco control have successfully asserted that tobacco is essential to the economic livelihoods of smallholder tobacco farmers and the economy of tobacco-growing countries. This nexus of economic, agricultural and public health policymaking is one of the greatest challenges facing tobacco control efforts, especially in LMICs. To date, there is a lack of comparative, individual level evidence about the actual livelihoods of tobacco-growing farmers and the political economic context driving tobacco production. This comparative evidence is critically important to identify similarities and differences across contexts and to provide local evidence to inform policies and institutional engagement. Our proposed four-year project will examine the economic situation of smallholder farmers in two major tobacco-growing LMICs-Mozambique and Zimbabwe-and the political economy shaping farmers' livelihoods and tobacco control efforts. We will collect and analyze the existing data and policy literature on the political economy of tobacco in these two countries. We will also implement household-level economic surveys of nationally representative samples of farmers. The surveys will be complimented with focus group discussions with farmers across the major tobacco-growing regions. Finally, we will interview key informants in these countries in order to illuminate the policy context in which tobacco production is perpetuated. The team will develop country-level reports and policy briefs that will inform two sets of dissemination workshops in each country with relevant stakeholders. We will also conduct workshops to present our findings to the survey and focus group participants, and other members of these tobacco-growing communities, so they can directly benefit from the research to which they are contributing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.011 |
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".