MétaCan
Menu
Back to cohort
Record W4411795068 · doi:10.1177/14705958251357448

Love without borders: The relationship between intercultural marriage and family-to-work enrichment

2025· article· en· W4411795068 on OpenAlexaff
Marlee Mercer, Souha R. Ezzedeen, Parbudyal Singh

Bibliographic record

VenueInternational Journal of Cross Cultural Management · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsYork University
Fundersnot available
KeywordsWork (physics)Social psychologyPsychologyIntercultural communicationSociologyCommunication

Abstract

fetched live from OpenAlex

Intercultural marriages have significantly increased in tandem with the global rise in migration and globalization, particularly in Western societies. While the advantages of diversity are widely celebrated in the workplace, the potential benefits that cultural diversity within a marriage might bring to professional environments remain underexplored. Despite numerous calls for in-depth analyses of close intercultural relationships, studies focusing on intercultural families are sparse. This conceptual paper investigates how intercultural marriages may foster family-to-work enrichment (FWE) through both instrumental and affective pathways. Utilizing critical contextual empiricism (CCE) and relational cultural theory (RCT), we propose a model that outlines how intercultural marriages can enhance enrichment from home to work. This model includes culturally diverse conflict management and communication skills, along with complementary characteristics that mutually enhance partners’ abilities. We suggest potential moderators of these effects and conclude by discussing theoretical and practical implications. In sum, our paper contributes to cross-cultural management research by identifying intercultural marriage as a context for cultural learning, which can enhance individuals’ capacity for empathy, adaptability, and relational competency in an ever-growing globalized world and work environment.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.046
GPT teacher head0.402
Teacher spread0.357 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Cross Cultural ManagementSame topicWork-Family Balance ChallengesFrench-language works237,207