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Interpersonal Context at Work and the Frequency, Appraisal, and Consequences of Boundary-Spanning Demands

2010· article· en· W2012064658 on OpenAlexaff
Paul Glavin, Scott Schieman

Bibliographic record

VenueSociological Quarterly · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInterpersonal communicationBoundary spanningWork (physics)PsychologyContext (archaeology)Work–family conflictSocial psychologyInterpersonal relationshipKnowledge management

Abstract

fetched live from OpenAlex

Compared to job-specific conditions, the interpersonal context of work has received less attention from work–family scholars. Using data from a 2007 U.S. survey of workers (N = 1,286), we examine the impact of workplace social support and interpersonal conflict on work–family conflict and exposure to boundary-spanning demands—as indexed by the frequency that workers receive work-related contact outside of normal work hours. Findings indicate that workplace social support is associated negatively with work-to-family conflict, while interpersonal conflict at work is associated with higher levels of work-to-family conflict. Results also indicate that both supportive and conflictive work contexts are associated with more frequent exposure to boundary-spanning demands. However, workers in supportive contexts are more likely to appraise these demands as beneficial for accomplishing work tasks, and are less likely to appraise them as disruptive to family roles. By contrast, workers in conflictive contexts are more likely to appraise demands as disruptive to family roles, and are less likely to appraise them as beneficial for paid work. Consequently, our findings underscore the resource and demands aspects of interpersonal work contexts and their implications for 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)0.001
metaresearch head score (Gemma)0.011
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.309
Teacher spread0.282 · 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

Citations33
Published2010
Admission routes1
Has abstractyes

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