MétaCan
Menu
Back to cohort
Record W2294230506 · doi:10.1177/0020715215625726

Gender ideology: The last barrier to women’s participation in political consumerism?

2015· article· en· W2294230506 on OpenAlexvenueno aff
Jasmine Lorenzini, Matteo Bassoli

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsConsumerismIdeologyPoliticsPolitical socializationSocioeconomic statusSocializationSociologyGender studiesPopulationPolitical scienceSocial psychologyPsychologySocial scienceAmerican political scienceDemographyLaw

Abstract

fetched live from OpenAlex

In this article, we analyze how gender affects women’s political participation. More specifically, we test the effect of gender ideology on young women’s participation in political consumerism. The current literature suggests different reasons to explain the gap in political participation between men and women, most importantly focusing on socioeconomic resources, gender roles, and political socialization, whereas little attention has been devoted to the individual interpretation of a woman and man’s own role in society. We test the effects of gender ideology on political consumerism, a form in which women participate more than men. We analyze political consumerism among young urban women, the population most likely to hold an egalitarian gender ideology. Moreover, we compare young women with different job conditions. Although the gender gap is closing or reversing in regard to specific forms of participation, such as consumerism, some inequalities remain, and our study contributes to understanding differences in participation among women themselves.

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)0.002
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.153
GPT teacher head0.472
Teacher spread0.318 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Comparative SociologySame topicGender Politics and RepresentationFrench-language works237,207