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Record W2911581129 · doi:10.1017/s1743923x18000818

A Gender Gap in Party Congruence and Responsiveness?

2019· article· en· W2911581129 on OpenAlexaff
Benjamin Ferland

Bibliographic record

VenuePolitics & Gender · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCongruence (geometry)IdeologyGender gapRepresentation (politics)DemocracyPolitical scienceSocial psychologyPsychologyPolitical economyDemographic economicsSociologyPoliticsLawEconomics

Abstract

fetched live from OpenAlex

Abstract Congruence and responsiveness between the policy preferences of citizens and elites are considered key characteristics of democracy. Although these relationships between citizens and elites have been thoroughly examined, little attention has been devoted to differences in the representation of women and men in studies of congruence and responsiveness. Herein, I evaluate the presence of a gender gap both in terms of party congruence and party responsiveness with respect to the relationship between female and male supporters and the party they voted for. In addition, I examine whether the presence of elected women in parties decreases the gender gap in party congruence and responsiveness. My analyses of the data from the Comparative Study of Electoral Systems and several national elections studies indicate that parties are generally as close and as responsive to the preferences of male supporters as to those of female supporters on the left–right ideological scale. However, the presence of elected women in parties favors women's representation and may thus reduce inequality in gender 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.084
GPT teacher head0.367
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations32
Published2019
Admission routes1
Has abstractyes

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