Feminization, rural transformation, and wheat systems in post-soviet Uzbekistan
Bibliographic record
Abstract
This paper examines how rural transformation in Uzbekistan alters gender norms and roles and, consequently, affects women's involvement in agriculture. We focus on the role that contextual factors, particularly kinship relations, government goals, and institutional structures each contribute to rural transformation and male out-migration, and how these, in turn, increase women's work in wheat production and processing. The wheat is the most important crop in the country which has the highest area coverage (35%) in Uzbekistan. We begin by highlighting the post-Soviet transition in Uzbekistan and its effects on the agricultural sector, including how households respond to opportunities for innovation. We then move to a discussion of our methodological approach drawing on insights from the GENNOVATE project, a collaborative initiative across 11 CGIAR centres that explored the relationship between changing gender norms in relation to women's roles in agricultural production and processing. Next, we examine an understudied topic in migration research i.e., how the transformation of agriculture contributes to increased dependence on unpaid female agricultural labour. We conclude with an analysis of how the feminization of agriculture alters household relations and women's participation in the public sphere. Significantly, we close with a reflection on what these changes mean for gender and innovation studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".