MétaCan
Menu
Back to cohort
Record W3000159853 · doi:10.1017/s0007123419000644

The Cultural Sources of the Gender Gap in Voter Turnout

2020· article· en· W3000159853 on OpenAlexaff
Ruth Dassonneville, Filip Kostelka

Bibliographic record

VenueBritish Journal of Political Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsUniversité de Montréal
FundersTampereen Yliopisto
KeywordsParliamentGender gapTurnoutVotingVoter turnoutContext (archaeology)Political sciencePoliticsDemographic economicsEconomicsLawGeography

Abstract

fetched live from OpenAlex

Abstract Recent publications argue that the traditional gender gap in voting has decreased or reversed in many democracies. However, this decrease may apply only to some types of elections. Building on prior studies, this article hypothesizes that although women participate at the same or higher rates than men in national elections, they participate less in supranational elections. The authors investigate this possibility empirically by analyzing the evolution of the gender gap in voter turnout in elections to the European Parliament (EP). The article makes three important contributions. First, it shows the presence and stability of the traditional gender gap in EP elections. Secondly, it finds that gender differences in political interest are the main source of this gender gap. Thirdly, these gender differences in political interest are, in turn, context dependent. They are strongly associated with cultural gender differences, which are captured through differences in boys’ and girls’ maths scores.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.061
GPT teacher head0.344
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

Citations49
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBritish Journal of Political ScienceSame topicGender Politics and RepresentationFrench-language works237,207