Gender and Political Representation
Bibliographic record
Abstract
In my dissertation, I present three papers that evaluate the causes of women’s political under-representation through a supply, demand, and institutions framework. First, I focus on elite demand. Drawing on theories of gender bias, group attachment, and partisan identity, I conduct a field experiment in Canada to examine whether political elites exhibit gender discrimination when responding to political aspirants. The results indicate that legislators are more responsive to female aspirants and more likely to provide them with helpful advice when they ask about how to get involved in politics. This pro-women bias, which exists at all levels of government, is stronger among female legislators and those associated with left-leaning parties. Next, I focus on mass demand. Drawing on theories of gender bias, gender stereotypes, and role congruity, I conduct a choice experiment in South Korea to examine how candidate sex and gender expression shape voter preferences. I find that voters, on average, prefer female candidates. Despite this pro-woman bias, however, voters don’t think that women will win the election. These results suggest that we shouldn’t necessarily infer voter behavior simply from voter preferences. When it comes to how voters evaluate candidates who deviate from gender norms, I find that voters tend to prefer candidates who run counter to gender stereotypes: they prefer women candidates who present a “tough” approach to politics and men candidates who present a “compassionate” approach. The third paper takes a more aggregate-level approach and looks at how supply-side and demand-side factors interact to affect women’s representation while controlling for institutional context. Existing empirical studies treat supply-side and demand-side factors separately and ignore the inherent interaction at the theoretical core of the supply and demand framework. However, women’s descriptive representation should only be high when supply and demand are both sufficiently high. I test the implications of my theory using a new global dataset on women’s representation from 1990 to 2018. The results are consistent with my theory and are substantively important because they indicate the conditions under which we can expect supply-side and demand-side factors to actually translate into greater female political representation.
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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 | 0.002 | 0.005 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".