Votes for Women: Electoral Systems and Support for Female Candidates
Bibliographic record
Abstract
It is a well-established finding that proportional representation (PR) electoral systems are associated with greater legislative representation for women than single member systems. However, the degree to which different types of PR rules affect voting for female candidates has not been fully explored. The existing literature is also hampered by a reliance on cross-national data in which individual vote preferences and electoral system features are endogenous. In this study, we draw upon an experiment conducted during the 2014 European Parliament (EP) elections to isolate the effects of different PR electoral systems. Participants in the experiment were given the opportunity to vote for real EP candidates in three different electoral systems: closed list, open list, and open list with panachage and cumulation. Because voter preferences can be held constant across the three different votes, we can evaluate the extent to which female candidates were more or less advantaged by the electoral system itself. We find that voters, regardless of their gender, support female candidates, and that this support is stronger under open electoral rules.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".