MétaCan
Menu
← Back to cohort
Record W2766636870 · doi:10.5465/ambpp.2016.243

Occupational Interactional Requirements and Work-Home Enrichment

2016· article· en· W2766636870 on OpenAlexaff
Devasheesh P. Bhave, Alexandru M. Lefter

Bibliographic record

VenueAcademy of Management Proceedings · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsMount Royal University
Fundersnot available
KeywordsVitalityPerceptionWork (physics)BusinessResource (disambiguation)PsychologyDomain (mathematical analysis)Computer scienceEngineering

Abstract

fetched live from OpenAlex

We examine how occupational interactional requirements influence work-home enrichment. We identify two manifestations of work-home enrichment: through objective indicators of how employees allocate their time at home, and through perceptual reflections of employees on their work-home enrichment. We conceptualize occupational interactional requirements as restorative properties of jobs that provide employees with resources that they transfer to the home domain. In terms of objective indicators, our results indicate that employees transfer these resources by spending more time in resource depleting activities of caring for household members, and less time in resource replenishing activities of socializing and relaxing. This suggests that occupational interactional requirements facilitate reallocating time in the home domain. In terms of perceptual reflections, we observe that occupational interactional requirements spark employees’ vitality, which enriches their life at home. Our results attest to considering workplace interactions as resource replenishing features of jobs that provide benefits across work and life boundaries.

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)0.005
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.026
GPT teacher head0.271
Teacher spread0.245 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueAcademy of Management Proceedings→Same topicJob Satisfaction and Organizational Behavior→French-language works237,207→