Peace Accords and the Adoption of Electoral Quotas for Women in the Developing World, 1990–2006
Bibliographic record
Abstract
The high percentage of women in Rwanda's parliament is well known. At 64%, it scores far above the world average of about 22% (IPU 2013). Rather than an anomaly, Rwanda is representative of many postconflict developing countries that feature women's political representation at above-average levels. A frequently identified correlate of this heightened representation has been the presence of electoral quotas for women (Bush 2011; Fallon, Swiss, and Viterna 2012; Paxton, Hughes, and Painter 2010). More generally, the role of societal rupture and transitions from conflict to peace or from authoritarianism to democracy have been a focus of gender and politics research in recent years (Fallon, Swiss, and Viterna 2012; Hughes 2007; 2009; Hughes and Paxton 2007; Viterna and Fallon 2008). Within such transitions, the role of women's participation has been identified as a key determinant of more beneficial posttransition outcomes for women (Viterna and Fallon 2008). Peace processes and the accords that they yield represent a mechanism through which transition and women's rights become linked and theoretically hold the potential to shape postconflict societies. However, the link between women's involvement in peace processes and the subsequent adoption of electoral quotas has not been explored. In this article, we seek to answer the question: What is the relationship between postconflict transition, peace processes, and quota adoption? To this end, we examine the role played by peace accords and, more specifically, accords with a focus on women's rights in leading countries to adopt electoral quotas for women.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".