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Record W2160161836 · doi:10.1177/0192513x10361866

Is There a Downside to Schedule Control for the Work-Family Interface?

2010· article· en· W2160161836 on OpenAlexaff
Scott Schieman, Marisa Young

Bibliographic record

VenueJournal of Family Issues · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHuman multitaskingScheduleWork–family conflictWork scheduleControl (management)PsychologyWork (physics)Association (psychology)Social psychologyComputer scienceEngineeringCognitive psychology

Abstract

fetched live from OpenAlex

Using data from a 2007 U.S. survey of workers, this article examines the implications of schedule control for work—family role blurring and work—family conflict. Four main findings indicate that (a) schedule control is associated with more frequent working at home and work—family multitasking activities; (b) the positive association between schedule control and multitasking suppresses the negative association between schedule control and work— family conflict; (c) the positive association between working at home and multitasking is weaker among individuals with greater schedule control; and (d) the positive association between work—family multitasking and work— family conflict is weaker among individuals with greater schedule control. Our findings reveal previously undocumented mediating, suppression, and moderating patterns in the ways that schedule control contributes to work—family role blurring and work—family conflict. The authors discuss the implications of these findings for views of schedule control as a “resource” and theories about the borders in the work—family interface.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.358
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations128
Published2010
Admission routes1
Has abstractyes

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