Gender, Leadership and Choice in Multiparty Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While a significant amount of research seeks to explain the comparative number of women in national legislatures, there is little research that examines the effects of women's leadership of political parties on voting behavior. This article brings together research on leadership effects in parliamentary elections and female candidate effects in legislative races. Ideological, structural, and situational differences between men and women have been used to explain gender gaps in voting. We explore an alternative explanation-gender identity When women candidates are present, the gender identity hypothesis assumes that women voters are more likely to choose women candidates because of gender. While this hypothesis has been tested in legislative races, it has not been applied to party leaders in parliamentary elections. We test the gender identity hypothesis in Australia, New Zealand, Canada and Britain. We find that leadership evaluations affect vote choice across all countries but the effects of gender and the combined effects of gender and leadership differ across countries.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| 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 it