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Record W7019406858

Gender and Political Representation

2023· dissertation· en· W7019406858 on OpenAlexaboutno aff

Bibliographic record

VenueOakTrust (Texas A&M University Libraries) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsRepresentation (politics)EliteFocus (optics)Affect (linguistics)Field (mathematics)Gender history
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.299
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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