Women's empowerment and education: Panchayats and women's Self-help Groups in India
Bibliographic record
Abstract
While women have made many advances, their inferior status to men continues to be a global phenomenon. At a time of unprecedented economic growth, India is experiencing a dramatic intensification of violence against women and the majority of girls are still not getting equal educational opportunity. In one of the most important steps for the empowerment of women, the Indian government gave constitutional status to village-level councils or Panchayati Raj institutions and reserved 33% of the seats in Panchayats for women. In addition, women were organized into Self-help Groups to mark the beginning of a major process of empowering women, although not much attention was paid to women's formal education. Our aim was to explore the impact of these measures on women's empowerment in the states of West Bengal and Mizoram. In general, we found that affirmative action does ensure that larger numbers of women enter politics but it does not ensure that the women participate in politics and function as elected representatives, because of lack of education. Empowerment needs to be seen as a holistic outcome of processes of critical education that enables women to lead autonomous lives and the freedom to act. Both affirmative action and education are necessary to empower women who have suffered discrimination and lack of power always.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".