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Record W2535792283 · doi:10.3138/jcfs.43.6.857

Intercultural Relationships: Entry, Adjustment, and Cultural Negotiations

2012· article· en· W2535792283 on OpenAlexvenueno aff
Luciana C. C. B. Silva, Kelly Campbell, David W. Wright

Bibliographic record

VenueJournal of Comparative Family Studies · 2012
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationMulticulturalismSocial psychologyPsychologyGlobalizationSociologyPolitical scienceSocial sciencePedagogy

Abstract

fetched live from OpenAlex

Globalization, technological advances, and increasingly flexible social norms have contributed to more widespread intercultural relationships, particularly in multicultural societies such as the United States. In this paper, the authors use Bronfenbrenner’s ecological systems framework (i.e., the macrosystem, exosystem, microsystem, chronosystem) to review the factors involved with entry and adjustment in intercultural relationships. Relationship entry is discussed in terms of how people of different cultural backgrounds meet, interact, and intimately relate. Factors that impact the likelihood of entering an intercultural relationship are outlined. Adjustment refers to the manner in which partners cope with the dyadic tensions that impact their relationship satisfaction and functioning. Compared to intracultural couples, intercultural couples are at a higher risk of experiencing adjustment problems over the course of the relationship. Therefore, suggestions are provided to help these couples minimize conflict and optimize satisfaction. Factors relating to relationship identity and cultural negotiation also are discussed. A summary of the most important clinical ideas to emerge from the literature on intercultural couples in the United States, as well as clinical suggestions for therapists working with these couples, are provided at the end of the paper.

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)0.000
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 score0.192
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.222
GPT teacher head0.488
Teacher spread0.266 · 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

Citations32
Published2012
Admission routes1
Has abstractyes

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