Boundary Management Permeability and Relationship Satisfaction in Dual-Earner Couples: The Asymmetrical Gender Effect
Bibliographic record
Abstract
Given the increasing use of technology and the growing blurring of the boundaries between the work and nonwork domains, decisions about when to interrupt work for family and vice versa can have critical implications for relationship satisfaction within dual-earner couples. Using a sample of 104 dual-earner couples wherein one of the partners is a member of the largest Italian smartphone-user community, this study examines how variation in boundary management permeability within dual-earner couples relates to partner relationship satisfaction, and whether the effect differed by gender and partners' agreement on caregiving roles in the family. Using actor-partner analysis, we examined the degree to which an individual and his or her partner's level of family-interrupting work behaviors (FIWB, e.g., taking a call from the partner while at work) and work-interrupting family behaviors (WIFB, e.g., checking work emails during family dinner) was positively related to relationship satisfaction. Results show that women experienced greater relationship satisfaction than men when their partners engaged in higher levels of FIWB, and this relationship was stronger when partners had perceptual congruence on who is primarily responsible for caregiving arrangements in the family. This study advances research on dual-earner couples by showing the importance of examining boundary management permeability as a family social phenomenon capturing transforming gender roles.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".