Taking Care into Account: Leveraging India's MGNREGA for Women's Empowerment
Bibliographic record
Abstract
ABSTRACT The potential of India's Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) for women's empowerment is immense. Studies examining gender‐related issues in MGNREGA have attested to the high levels of participation of women on worksites, and their positive experiences of working in MGNREGA. This article argues, however, that an exclusive focus on increased participation of women does not serve an agenda of promoting ‘women's empowerment’. By ignoring the dynamics and processes of unpaid care work, both the making and the implementation of the Act fall short of the goal of women's empowerment. The author argues that this invisibilizing of care arises from the gendered nature of the interactions of formal and informal institutions that have shaped MGNREGA. The article examines the gendered debates during the formulation of the Act and analyses the gendered nature of its implementation. It concludes that a true focus on women's empowerment requires that women's lived experiences are taken into account, especially those relating to their unpaid care responsibilities. MGNREGA's potential for women's empowerment can only be achieved through adequate implementation and monitoring of its gender provisions, which in turn depend on changing the formal and informal institutions that underpin policy processes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".