Are wheat-based farming systems in South Asia feminizing?
Bibliographic record
Abstract
This article pulls together the state of knowledge on the degree to which wheat-based systems in Bangladesh, India, Nepal, and Pakistan, are feminizing. It is not yet possible to make definitive statements. However, it is clear that wheat-based systems are undergoing far-reaching changes in relation to “who does what” and “who decides.” There are some commonalities across all four countries. Intersectionalities shape women’s identities and abilities to exert their agency. Purdah is a cultural norm in many locations. Nevertheless, each country displays different meta-trends. In Nepal managerial feminization is increasing unlike in Pakistan. Women in Bangladesh spend the least time in field work whereas in other countries they are often strongly engaged. There are strong local variations within countries as well which we explore. Establishing the extent of feminization is challenging because studies ask different questions, operate at different levels, and are rarely longitudinal. Researchers often construct men as primary farmers, leading to a failure to find out what men and women really do and decide. This diminishes the value of many studies. Cultural perceptions of honor can make men respondents reluctant to report on women’s agency and women can be reluctant to claim agency openly. We provide suggestions for better research, and urge support to women as workers and decision-makers.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".