Heterogeneity in male and female farmers’ preference for a profit‐enhancing and labor‐saving technology: The case of Direct‐Seeded Rice (DSR) in India
Bibliographic record
Abstract
Abstract Labor‐saving and income‐increasing technologies may affect women farmers differently from men. However, very few studies explicitly account for women's preferences for new technologies. We carried out a discrete choice experiment with 337 female and 329 male farmers in Maharashtra, India, to measure their willingness to pay (WTP) for direct‐seeded rice (DSR) with drum seeder and to understand the gender differences in marginal valuations of key attributes. We used the Women's Empowerment in Agriculture Index (WEAI) to collect self‐reported data on the role and say of women in different domains of decision making. The respective gender roles of women and men in the family and on the farm are aligned with their preferences. Men have a greater say over how the family spends the cash. Accordingly, men tend to have a higher WTP for attributes that increase income (increase in yield) or reduce cash costs (reduction in seed rate). Women contribute a large share of the labor for transplanting rice, much of which is unpaid work on family farms. Women, therefore, seem to value labor saving more. Women in our sample were more interested in the new technology and had a higher WTP for it.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".