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Record W3093307577 · doi:10.1177/1948550620962653

Divided Together: How Marginalization of Intercultural Relationships Is Associated With Identity Integration and Relationship Quality

2020· article· en· W3093307577 on OpenAlexafffund
Maya A. Yampolsky, Alexandria L. West, Biru Zhou, Amy Muise, Richard N. Lalonde

Bibliographic record

VenueSocial Psychological and Personality Science · 2020
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsMcGill UniversityYork UniversityUniversité Laval
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologySocial psychologyIdentity (music)Intercultural communicationRomanceQuality (philosophy)Intercultural relationsCommunication

Abstract

fetched live from OpenAlex

Despite the growing prevalence of intercultural romantic relationships—in which partners identify with different racial, national, or religious backgrounds—people in intercultural relationships still face marginalization and disapproval from others. Relationship marginalization sends a message to couples that they do not belong together, and partners may feel that their cultural identity and their relationship are disconnected. Two studies—one study of people in intercultural relationships and one of both members of intercultural couples—showed that when people perceived greater relationship marginalization, they were more likely to separate their couple identity from their cultural identity or believe they had to choose between these identities and they were less likely to integrate these identities. Less integration and more separation between a person’s couple and cultural identities was associated with lower relationship quality for both partners. The findings suggest that marginalization can create challenges for the maintenance and quality of intercultural relationships.

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)0.001
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 score0.136
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.211
GPT teacher head0.450
Teacher spread0.240 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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