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Record W2140766866 · doi:10.1177/0022022112463604

When Ethnic Identities Vary

2012· article· en· W2140766866 on OpenAlexafffundabout
Rui Zhang, Kimberly A. Noels

Bibliographic record

VenueJournal of Cross-Cultural Psychology · 2012
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSituational ethicsIdentity (music)Ethnic groupSituatedFeelingMulticulturalismSocial psychologyVariation (astronomy)Affect (linguistics)PsychologyNormativeCultural identitySociologyPolitical scienceAnthropologyCommunicationAesthetics

Abstract

fetched live from OpenAlex

The current research investigated situational variations in ethnic identity and the relations between identity variations and psychological well-being. In a sample of first- ( n = 47) and second-generation ( n = 82) immigrants to Canada who completed a questionnaire survey, it was found that Canadian and heritage identity variation showed the hypothesized situational and generational differences. Furthermore, heritage affect and heritage ties buttressed the second-generation group from experiencing negative well-being as a result of differences in heritage identity across private and public domains (i.e., cross-situation variation). Finally, the relations between situated Canadian or heritage identity (i.e., within-situation variation) and well-being in both generations depended on the felt authenticity associated with the given identity. In general, our results showed positive effects of feeling true to a counternormative (vs. normative) identity on well-being. These patterns were interpreted in terms of the normative implications of the situated identity choices. Overall, the results underscore the importance of examining when and how identity variation is psychologically adaptive or maladaptive in multicultural contexts.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.236
GPT teacher head0.529
Teacher spread0.293 · 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; both teacher heads agree on what is shown here.

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

Citations39
Published2012
Admission routes3
Has abstractyes

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