A dynamic framework of boundary permeability: daily events and within-individual fluctuations in daily work and nonwork boundary permeation
Bibliographic record
Abstract
How do daily experiences affect work-nonwork boundaries? In this paper, we present a dynamic framework of boundary permeability that aims to answer this question. We propose that daily events are associated with increases in permeation across daily work and nonwork boundaries, and that these increases are strongest on the days when the events occur. We further argue that support seeking and social capitalization are critical interpersonal processes that provide additional insights into the relations between daily events and daily boundary permeation. In Study 1, working parents (N = 88) completed a paper diary for seven days (612 observations), with repeated measures of daily events (hassles and uplifts) and daily work and nonwork boundary permeation. In Study 2, employed individuals (N = 138) completed a similar diary on-line (834 observations) that also included measures of the interpersonal processes. Results of both studies were generally consistent with our hypotheses, and offered support for our dynamic framework. At a practical level, our findings reinforce the importance of incorporating flexibility into human resources practices that are meant to support employees’ efforts to manage the work-nonwork interface – because every day brings about new events.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".