Explaining women's political underrepresentation in democracies with high levels of corruption
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
Abstract Many democracies with high levels of corruption are also characterized by low levels of women's political representation. Do women candidates in democracies with high levels of corruption face overt voter discrimination? Do gender dynamics that are unique to highly corrupt, democratic contexts influence citizens’ willingness to vote for women? We answer these questions using two separate sets of experiments conducted in Ukraine: two vignette experiments and a conjoint analysis. In line with existing cross-sectional research on Ukraine, our experiments reveal little evidence of direct voter bias against women candidates. Our conjoint analysis also offers novel insights into the preferences of Ukrainian voters, showing that both men and women voters place a great deal of value in anti-corruption platforms, but voters are just as likely to support women and men candidates who say they will fight corruption. Our analysis suggests that women's political underrepresentation in highly corrupt contexts is driven more by barriers that prevent women from winning party nominations and running for office in the first place, rather than overt discrimination at the polls.
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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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| 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