Why Do Farmers Grow Tobacco? A Qualitative Exploration of Farmers Perspectives in Indonesia and Philippines
Bibliographic record
Abstract
Tobacco supply remains a pressing challenge to tobacco control. Tobacco remains a dominant cash crop in many low- and middle-income countries, despite the evidence suggesting that it is not as profitable as industry claims and is harmful to health and the environment. In order to implement successful and sustainable alternative livelihood interventions, it is important to understand why farmers continue to grow tobacco. This study explores this question from the perspective of farmers in Indonesia and Philippines. This study was informed by interpretive description methodology. Data was collected through focus group discussions (FGDs) (n = 7) with farmers (n = ~60). The FGDs were audio recorded, transcribed verbatim, and then translated into English. An inductive thematic analysis of the data was conducted to identify and categorize the reason provided by participants. We identified two overarching themes: (1) perceived viability (profitability, ready market, and environmental factors) and (2) financial context. Financial context included lumpsum payments and access to financial loans and credit facilities in light of their lack of capital. These results highlight that, in addition to identifying viable alternatives to tobacco, institutional factors such as improved access to credit and well-developed supply chains are key to the successful uptake of alternative livelihoods.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".