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Record W2908771593 · doi:10.1002/ejsp.2566

Identity configurations and well‐being during normative cultural conflict: The roles of multiculturals’ conflict management strategies and academic stage

2019· article· en· W2908771593 on OpenAlexafffund
Melisa Arias‐Valenzuela, Catherine E. Amiot, Andrew G. Ryder

Bibliographic record

VenueEuropean Journal of Social Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsConcordia UniversityUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIntrapersonal communicationPsychologyIdentity (music)NormativeSocial psychologyCompartmentalization (fire protection)Conflict managementCultural conflictCultural identitySociologyInterpersonal communicationEpistemologySocial science

Abstract

fetched live from OpenAlex

Abstract Multiculturals encounter normative cultural conflicts (intrapersonal conflicts between their cultures’ norms). Yet, no research has examined how these conflicts are managed, nor their antecedents and repercussions. This article examined how these conflicts are managed using two sets of conflict management strategies (active and agreeable) and tested whether they mediate the associations between identity configurations and well‐being. Also, as the benefits of having integrated selves typically increase in later life stages, this article examined whether the associations between identity configurations and well‐being differ between earlier (pre‐university) and later academic stages (university). In Study 1 ( N = 235), active strategies mediated the link between identity integration and well‐being, whereas agreeable strategies mediated the link between compartmentalization and ill‐being. In Study 2 ( N = 241), these results were replicated. Study 2 further showed that the association between identity integration and well‐being was stronger among university than pre‐university students. Implications of these results are discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.841
Threshold uncertainty score0.420

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 communication0.0000.001
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 head0.086
GPT teacher head0.402
Teacher spread0.316 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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