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Mediated ambition? Gender, news and the desire to seek elected office

2019· article· en· W2987053068 on OpenAlexaff
Scott Pruysers, Melanee Thomas, Julie Blais

Bibliographic record

VenueEuropean Journal of Politics and Gender · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsCarleton UniversityUniversity of CalgaryDalhousie University
Fundersnot available
KeywordsPoliticsMedia coveragePolitical scienceGender gapSubject (documents)Media useSocial psychologyPsychologySociologyDemographic economicsMedia studiesLawEconomics

Abstract

fetched live from OpenAlex

A gender gap in nascent political ambition is among the best documented in political behaviour. Although the reasons for this gender gap are numerous, it has often been speculated that the media might be partly responsible for suppressing ambition among women. Considerable evidence shows that women in politics are generally subject to more attention being paid to their appearance, marital status and sex than their male counterparts. Does this kind of media coverage dampen political ambition? We test this possibility in two experimental studies. In Study 1, we explore whether overtly sexualised coverage regarding the appearance of politicians can dampen political ambition, while Study 2 considers whether media coverage focusing on the private lives of politicians (specifically parental/marital status) affects political ambition. This two-study approach allows us to consider whether media coverage of political officials is related to political ambition and whether different types of media coverage have different effects.

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)0.005
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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.043
GPT teacher head0.300
Teacher spread0.256 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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