Women in panchayath raj institutions: a new tool for political participation
Bibliographic record
Abstract
Women political participation topic is current issue in all over the world. According World human Development Index 2015 report Sweden, Denmark, Norway, Netherlands, Canada, Finland, Australia and Switzerland are the best countries for Women. India stands 130th rank in same report. What is it means? Specialist says that Educational, Economical, and political areas are needed to achieve women empowerment. Political women empowerment is most complicated area. But it is very much needed. So what is the situation of political empowerment of women in India? I am trying to find the answer in this article. It is talks about women how participating in Indian mainstream political of two levels one is upper and another one are Panchaythi levels. Statistical and analytical details are available but it is not a comparative article. But the statistics shows deference and scope between reservation and non reservation political areas for women. The main focus is on ‘objective and current status of women political scenario in India.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".