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Record W4205868370 · doi:10.1108/pr-01-2021-0006

A qualitative investigation of the work-nonwork experiences of dual-career professional couples without children

2022· article· en· W4205868370 on OpenAlexaffabout
Galina Boiarintseva, Souha R. Ezzedeen, Anna McNab, Christa L. Wilkin

Bibliographic record

VenuePersonnel Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsYork University
Fundersnot available
KeywordsThrivingPsychologySalience (neuroscience)Social psychologyRole conflictCareer developmentSociology

Abstract

fetched live from OpenAlex

Purpose This paper aims to investigate the idiosyncratic relationships between work and nonwork among dual-career professional couples (DCPCs) intentionally without children, considering individual members' role salience, nonwork responsibilities and care or career orientation. Design/methodology/approach Interview data from 21 Canadian and American couples (42 individuals) was used to explore the research question: How do DCPCs without children perceive their work-nonwork balance? Findings DCPCs without children are a heterogenous demographic. Some couples are career oriented, some care oriented, some exhibit both orientations, shaping their experience of work-nonwork balance. Unlike popular stereotypes, they do have nonwork responsibilities and interests outside of their thriving careers. Similar to their counterparts with children, they face conflicts managing work and nonwork domains. Originality/value Based on theories of role salience, work-nonwork conflict, enrichment and balance, the authors suggest that analyses of work-nonwork balance should include nonwork activities other than child caring. The authors further propose that the experience of the work-nonwork interface varies according to whether couples are careerist, conventional, non-conventional or egalitarian. The study also demonstrates that work-nonwork experiences are relational in nature and should be explored beyond a strictly individual perspective.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.080
GPT teacher head0.370
Teacher spread0.289 · 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 designQualitative
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

Citations4
Published2022
Admission routes2
Has abstractyes

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