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Record W2462208519 · doi:10.1177/1069397116659048

Locals’ Bidimensional Acculturation Model

2016· article· en· W2462208519 on OpenAlexaboutno aff
Katharina Lefringhausen, Tara C. Marshall

Bibliographic record

VenueCross-Cultural Research · 2016
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsnot available
Fundersnot available
KeywordsAcculturationMulticulturalismAdaptation (eye)Confirmatory factor analysisPsychologySample (material)Social psychologyStructural equation modelingCultural diversityPsychological adaptationDiversity (politics)SociologyEthnic groupStatisticsAnthropologyMathematics

Abstract

fetched live from OpenAlex

Across two studies, we tested whether members of host communities (i.e., locals) can themselves simultaneously maintain their national culture maintenance and adapt toward cultural diversity (i.e., multiculturalism) in their own home country, supporting a bidimensional model of acculturation, or whether these strategies are incompatible, supporting a unidimensional model of acculturation. We modified the Vancouver Index of Acculturation (Multi-VIA) to assess locals’ national culture maintenance and multicultural adaptation within their own home country. Study 1 supported the bidimensionality of the Multi-VIA in an American sample ( n = 218). Moreover, we found an oblique association between locals’ national culture maintenance and multicultural adaptation. In Study 2, we tested the Multi-VIA’s psychometric properties across three continent groups (North America, Europe, and Asia; N = 619). Multiple-group confirmatory factor analysis demonstrated good model fit for the entire sample. Nevertheless, the association between national culture maintenance and multicultural adaptation was orthogonal for Asians and oblique for Americans and Europeans. In addition, national culture maintenance predicted higher levels of locals’ life satisfaction, whereas multicultural adaptation was associated with less acculturative stress and greater intercultural sensitivity.

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 score0.759
Threshold uncertainty score0.997

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.005

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 head0.465
GPT teacher head0.584
Teacher spread0.119 · 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

Citations58
Published2016
Admission routes1
Has abstractyes

Explore more

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