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 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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.059 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.034 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".