Bibliographic record
Abstract
Increasing levels of democratic freedoms should, in theory, improve women’s access to political positions. Yet studies demonstrate that democracy does little to improve women’s legislative representation. To resolve this paradox, we investigate how variations in the democratization process—including pre-transition legacies, historical experiences with elections, the global context of transition, and post-transition democratic freedoms and quotas—affect women’s representation in developing nations. We find that democratization’s effect is curvilinear. Women in non-democratic regimes often have high levels of legislative representation but little real political power. When democratization occurs, women’s representation initially drops, but with increasing democratic freedoms and additional elections, it increases again. The historical context of transition further moderates these effects. Prior to 1995, women’s representation increased most rapidly in countries transitioning from civil strife—but only when accompanied by gender quotas. After 1995 and the Beijing Conference on Women, the effectiveness of quotas becomes more universal, with the exception of post-communist countries. In these nations, quotas continue to do little to improve women’s representation. Our results, based on pooled time series analysis from 1975 to 2009, demonstrate that it is not democracy—as measured by a nation’s level of democratic freedoms at a particular moment in time—but rather the democratization process that matters for women’s legislative representation.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".