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Record W2922124875 · doi:10.1111/jcms.12848

Women's Representation across Different Generations: A Longitudinal Analysis of the European Parliament

2019· article· en· W2922124875 on OpenAlexaff
Daniel Stockemer, Aksel Sundström

Bibliographic record

VenueJCMS Journal of Common Market Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsParliamentRepresentation (politics)European unionMaterialismFirst generationPolitical sciencePoliticsSociologyDemographyLawEconomicsEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract In this article, we focus on generational differences in women's representation and hypothesize that younger generations of women should be more highly represented than older generations, both in general and within the same parliament. We tested this hypothesis with data on all members who have ever served in the European Parliament since 1979. Of the four generations who have ever served in Brussels and Strasbourg – the World War II generation, the 1968 generation, the post‐1968 generation and the post‐materialist generation – we found both that women's representation increased with every generation and that their representation differs between generations within the same parliament. Finally, our results indicate that while these processes occurred roughly one generation later in eastern and southern Europe, yet, they happened faster in these parts of the European Union.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.077
GPT teacher head0.391
Teacher spread0.314 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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