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Record W4386945583 · doi:10.1111/jopy.12889

Daily agreeableness and acculturation processes in ethnic/racial minority freshmen: The role of inter‐ethnic contact and perceived discrimination

2023· article· en· W4386945583 on OpenAlexafffund
Yiqun Wu, Jingyi Xu, Yishan Shen, Yijie Wang, Yao Zheng

Bibliographic record

VenueJournal of Personality · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsAgreeablenessAcculturationEthnic groupPsychologyPersonalitySocial psychologyInterpersonal communicationBig Five personality traitsClinical psychologyDevelopmental psychologySociology

Abstract

fetched live from OpenAlex

OBJECTIVES: Having higher levels of mainstream cultural orientation (MCO), an important component of acculturation attitudes and behaviors, is beneficial for ethnic/racial minority students during the transitions into university. Scant research has investigated MCO at a micro daily timescale. This study examined how personality (agreeableness) functions in conjunction with interpersonal processes (inter-ethnic contact and perceived discrimination) to influence MCO as daily within-person processes. METHODS: Multi-level structural equation modeling were used to analyze month-long daily diary data from 209 ethnic/racial minority freshmen (69% female). RESULTS: There was a positive indirect association between agreeableness and MCO through inter-ethnic contact at both within- and between-person levels. At the within-person level, on days with lower (vs. higher) levels of ethnic/racial discrimination, higher levels of agreeableness were associated with higher levels of MCO. CONCLUSIONS: These findings highlight the contributions of intensive longitudinal data in elucidating ethnic/racial minority students' personality and acculturation processes in daily life involving protective and risk factors on micro timescales.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.398
Teacher spread0.312 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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