In-and-Out of Tobacco Farming: Shifting Behavior of Tobacco Farmers in Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding the variables that affect farmers' decisions as to whether to grow tobacco and/or other crops provides important insights into their economic lives and can help to inform the development and implementation of policies that shape both tobacco production and tobacco control, such as increasing tobacco excise taxes. This study employs complementary quantitative and qualitative methodologies to identify variables that affect tobacco farmers' economic decision making in Indonesia, a major tobacco producer. The research focuses on the variables that affect tobacco farmers' decisions to continue tobacco farming or shift to non-tobacco farming. It finds that tobacco farmers' decision making is complex but also predictable. The results of the quantitative analysis suggest that farming profits and positive rainfall shocks are two of the key variables that affect the decision to cultivate tobacco. The qualitative results confirm these findings and further illuminate that access to credit, education (agricultural and otherwise) and information play substantial roles in farmers' economic decision making. Most of these variables are affected by the unequal relationship between the tobacco firms that buy tobacco and the farmers, wherein the farmers are consistently at a disadvantage in terms of negotiating key parameters such as prices and evaluation of leaf quality.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it