Taking Care into Account: Leveraging India's MGNREGA for Women's Empowerment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it