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Record W45138591 · doi:10.3138/jcfs.43.2.165

Work-Family Conflict in the Nordic Countries: A Comparative Analysis

2012· article· en· W45138591 on OpenAlexvenueno aff
Ida Öun

Bibliographic record

VenueJournal of Comparative Family Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsWork–family conflictWelfare stateWork (physics)WelfareDivision of labourFamily lifeDemographic economicsState (computer science)SociologyPolitical sciencePsychologyEconomicsGender studiesPolitics

Abstract

fetched live from OpenAlex

The aim of this article is to examine men’s and women’s subjective experiences of work-family conflict in the Nordic welfare states. These countries are often considered to be frontrunners with regard to gender equality, especially regarding the provision of policies that aim to support the reconciliation between work and family life. However, previous research has produced divergent results in response to the question of whether the welfare state institutions of the Nordic countries help to reduce work-family conflict. Do supportive institutions matter, or is the household division of labour of greater importance regarding experiences of work-family conflict? Drawing on data from the 2002 module of the International Social Survey Programme, the analyses indicate that experiences of work-family conflict among Nordic men and women can be divided into three clusters: work-family balance, occupational work overload, and dual work overload. In spite of their shorter working hours, women experience higher levels of work-family conflict than men. An unfair division of housework also increases work-family conflict. In the main, experiences of work-family conflict do not differ greatly among the Nordic countries, with the exception of Finland, where the level is lower than in the other countries. This points to a difference within the Nordic welfare state regime regarding the transition towards 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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.187
GPT teacher head0.425
Teacher spread0.238 · 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

Citations33
Published2012
Admission routes1
Has abstractyes

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