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Record W2792502126 · doi:10.3917/rfsp.676.1067

L’impact du genre sur le vote Marine Le Pen

2018· article· fr· W2792502126 on OpenAlexaff
Abdelkarim Amengay, Anja Durovic, Nonna Mayer

Bibliographic record

VenueRevue française de science politique · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

Le « Radical Right Gender Gap » désigne la moindre propension des femmes à voter pour les droites radicales. C’était vrai du temps de Jean-Marie Le Pen, moins depuis que sa fille lui a succédé. À l’élection présidentielle de 2012, les électrices ont autant voté pour elle que les électeurs, toutes choses égales par ailleurs. Puis l’écart est revenu aux élections intermédiaires qui ont suivi et il a de nouveau disparu à l’élection présidentielle de 2017. Cet article cherche à comprendre ces variations en croisant les effets du genre, de l’appartenance générationnelle et des traits de personnalité sur le vote Le Pen, à partir des French Election Studies (1988-2017).

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)0.016
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 score0.036
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0360.004

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 head0.112
GPT teacher head0.414
Teacher spread0.301 · 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

Citations62
Published2018
Admission routes1
Has abstractyes

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