Voter Sexism and Electoral Penalties for Women Candidates: Evidence from Four Democracies
Bibliographic record
Abstract
Abstract Recent experimental research suggests that when women stand as political candidates, they often enjoy more support amongst voters than men. However, women remain under-represented in politics worldwide, and observational research suggests sexism is prevalent and consequential for voter behaviour. Here, we attempt to bridge these contradictory findings and offer observational evidence of approximately 26,000 voters and 5,346 candidates in Australia, Canada, Britain, and the USA. American voters are slightly more likely to vote for a woman than a man, but we find no evidence of gender preference in the other countries. Interestingly, although sexism is prevalent in all four countries, we find no evidence for an effect of voter sexism on support for women candidates. We do find evidence that abstention, at least in the USA, is an important electoral choice for sexist partisans faced with a woman co-partisan candidate.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".