MétaCan
Menu
Back to cohort

The Hidden Cognitive Side of Work-Family Boundary Management

2024· article· en· W4400443961 on OpenAlexaff
Victoria Daniel, Yujie Zhan

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsWilfrid Laurier UniversityYork University
Fundersnot available
KeywordsWork (physics)Boundary (topology)CognitionBoundary-workPsychologyComputer scienceMathematicsEngineeringSociologyNeuroscienceMathematical analysisMechanical engineering

Abstract

fetched live from OpenAlex

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.308
Teacher spread0.280 · 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 designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAcademy of Management ProceedingsSame topicWork-Family Balance ChallengesFrench-language works237,207