22. The Feminization of Agricultural Labour in India, With a Case Study of the Semi-Arid Region of Andhra Pradesh: A Means of Empowerment or a Method of Domination?
Bibliographic record
Abstract
With the general shift of men turning to out-migration work in times of economic disparity, women in rural India, specifically in the region of Andhra Pradesh, are forced to step in and fill the gap in agricultural labour left by migrating men. This phenomenon, coupled with the increased desire for female agricultural labourers – because of their tolerance of low wages – has led to a significant increase in the feminization of agricultural labour in India since the 1990s. While neoliberal writers argue that the increasingly feminized workforce of agricultural labour in rural India is largely demand-driven – both by male-out migration and thus the freeing up of agricultural work for women, I will argue, in accordance with the Marxist-feminist school of thought, that the increased feminization of agricultural labour in rural Andhra Pradesh does not reflect rural prosperity, but in fact is the “consequence of increasing pauperization among the small peasantry” (Garikipati 2008:630). This paper will explore the debate of whether or not the feminization of the agricultural workforce in rural Andhra Pradesh has accelerated female independence and empowerment in both the private (household) and public spheres. This locality study will thus add to a critical Marxist-feminist perspective of the feminization of agricultural labour in India generally, and the semi-arid region of Andhra Pradesh specifically, while raising the question of who truly benefits form the feminization of the agricultural workforce.
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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 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".