The role of women’s descriptive representation on same-gender and proximity voting among women
Bibliographic record
Abstract
The article examines the relationship between the descriptive representation of women in political parties and its effect on same-gender and proximity voting among women. In particular, we examine whether women are more likely to support parties that have more women representatives or a woman party leader. We also consider whether women’s descriptive representation in parties may help women cast a vote that better corresponds to their preferences. To answer these questions, we make use of data from the Comparative Study of Electoral Systems. We analyse respondents’ vote choice in 55 elections across 14 countries. Overall, our results show that the presence of a woman party leader motivates women to support such a party and that it also encourages women to vote for a party closer to their ideological position. The results, however, do not provide strong evidence that the presence of more women representatives in parties also fosters same-gender and proximity voting among women.
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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 | 0.003 | 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".