A qualitative investigation of the work-nonwork experiences of dual-career professional couples without children
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".