Bibliographic record
Abstract
On a global scale, India ranks very poorly in terms of gender equality. This overall indicator masks important heterogeneities across the separate measures of female empowerment. India scores very highly with regards to equality of civil liberties and the political participation of women. But the country falls well below the global average with respect to equal access to economic resources and protection from gender-based violence. These poorer empowerment indicators have persisted not only in the wake of strong economic progress, but also in the context of an impressive set of government led reforms and policies targeting women. These different initiatives have successfully augmented women’s agency in both the private and public spheres of life, but women and girls still face extreme discrimination and violence. The salience of restrictive local customs appears to be a core hindrance towards transformative change. This paper reviews the economics literature which examines this complexity across the different dimensions of female empowerment in India. It highlights the newly emerging research focused on ameliorating gender biased norms and discusses potential steps forward. JEL Classifications: J12, J16
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".