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Record W3000399047 · doi:10.1177/0020715219893750

Gender equality and work–family conflict from a cross-national perspective

2019· article· en· W3000399047 on OpenAlexvenueno aff
Gayle Kaufman, Hiromi Taniguchi

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsEgalitarianismIdeologyGender equalityPerspective (graphical)Gender inequalityWork–family conflictInequalitySocial psychologySurvey data collectionPsychologyWork (physics)Gender studiesSociologyPolitical science

Abstract

fetched live from OpenAlex

This study examines the relationship between gender ideology at the individual level, gender equality at the country level, and women and men’s experiences of work interference with family (WIF) and family interference with work (FIW). We use data from the 2012 International Social Survey Programme as well as the 2011 to 2015 Human Development Reports. Our sample consists of 24,547 respondents from 37 countries. Based on multilevel mixed-effects logistic models, we find that women are more likely than men to experience WIF and FIW. At the individual level, traditional gender ideology positively predicts WIF and FIW. Women and men who reside in more gender-unequal countries have a higher likelihood of FIW while men in these contexts also are more likely to experience WIF. Societal gender inequality is more consequential for those who hold less traditional gender ideology. In conclusion, gender egalitarianism at the individual level and gender equality at the country level are both associated with less WIF and FIW. Policies that seek to address work–family balance should incorporate measures to promote gender equality.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.212
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.156
GPT teacher head0.456
Teacher spread0.299 · 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 teacher head, 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

Citations30
Published2019
Admission routes1
Has abstractyes

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