The Hidden Cognitive Side of Work-Family Boundary Management
Bibliographic record
Abstract
Work-family life is becoming increasingly complex for the modern working parent, making boundaries that define aspects of the interface evermore important yet presenting their own challenges in ongoing work-family management. However, the boundary management literature has emphasized general preferences or tendencies that behaviorally integrate or segment work and family, and predominantly treated these boundary constructs as stable. Consequently, little is known about the essence of doing boundary work as a practice in and of itself. We therefore sought to understand how people construct, control, and change their boundaries by taking an inductive approach to explore the experiences of remote working parents who had to undertake the full-time care of their children during the pandemic. Taking a grounded theory approach to analyze two distinct sources of qualitative data, we uncover the cognitive nature of boundary work that encompasses multiple stages: anticipating boundary needs, boundary planning, regulating boundary implementation, and adaption of boundaries. Altogether, we incorporate existing research with these findings to build new theory of boundary work as a thoughtful and effortful process that unfolds through pre- to post-enactment phases—with these direct and novel explanations for how and why people manage their boundaries having salient theoretical and practical implications.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.050 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".