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Record W2412155244 · doi:10.5539/ibr.v9n8p24

A Matter of Love: Exploring What Enables Work-family Enrichment

2016· article· en· W2412155244 on OpenAlexvenueno aff
Sowon Kim, Mireia Las Heras, María José Bosch

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsAgapeSpousePsychologyQualitative researchWork (physics)Order (exchange)Resource (disambiguation)Qualitative analysisSocial psychologyManagementSociologyBusinessSocial scienceEconomicsFinanceTheologyEngineeringComputer science

Abstract

fetched live from OpenAlex

The purpose of this empirical study is to examine the conditions under which work-family enrichment happens. We conducted a total of 30 interviews with managers (and their spouses) participating in a demanding executive education program at a prestigious business school in Spain in order to explore how work and family resources are generated and transferred from one role to the other. Based on the qualitative results, we developed a model and surveyed 302 Chilean employees across an organization in the industrial sector in order to test our preliminary results in the qualitative stage. In our qualitative study, we find that there is a unique resource generated only in the family domain, which we define as “agape love” that contributes to enrichment. Our quantitative study confirms that, the more individuals experience agape love from spouse and children, the more the family enriches the employee’s work life.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0050.005
Open science0.0010.005
Research integrity0.0010.001
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 head0.149
GPT teacher head0.390
Teacher spread0.241 · 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 designQualitative
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

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