Bibliographic record
Abstract
The debate on the impact of women's proportional strength in decision-making bodies has focused mainly on whether it affects public policy priorities, in the context of western legislatures. This focus misses the in-between process: the impact of women's numbers on their effective participation, such as attending meetings, speaking up at them, and holding office. There is also a dearth of rigorous empirical analysis which controls for factors other than gender. This chapter provides a typology of participation, examines the extent to which women are participating in different activities within community forestry institutions in India and Nepal, and statistically examines whether a group's gender composition affects women's effective participation. It also tests for any critical mass effects. The results support the popularly emphasized proportions of one-quarter to one-third, but women's economic position also matters. On office bearing, going towards gender parity further improves a woman's chances of holding office, as do her personal attributes, such as being literate and single.
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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 | 0.000 | 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 | 0.001 | 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 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".