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The social identity and psychology of mixed-race individuals: An international study

2025· article· en· W4411627868 on OpenAlexafffundabout
Mark Cleveland

Bibliographic record

VenueInternational Journal of Intercultural Relations · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsRace (biology)PsychologyMixed raceSocial psychologyIdentity (music)Social identity theorySociologyGender studiesSocial group

Abstract

fetched live from OpenAlex

Immigration drives population growth in most Western countries. The resulting cultural diversity is accompanied by a sharp rise in mixed-race unions and people with mixed-race heritage. Many studies have investigated ethnic and racial identity and to account for their impact on self-concept, cognition, emotions, and behaviors. Relative to their monoracial counterparts, mixed-race individuals face additional challenges when constructing and expressing their ethnic and racial identity, and these processes are further complicated by how others perceive and label them. Research into mixed-race social identity and the predictors and psychological outcomes of this identity, is still in its infancy. With data gathered from mixed-race individuals living in three countries (Canada, USA, UK), a second order factor structure for operationalizing multidimensional character of mixed-race identity (MRI) is tested. I then investigate how various aspects of MRI are informed by the minority-parent’s ethnic maintenance, and by independent and interdependent self-construals. I also examine how MRI affects collective self-esteem and life satisfaction , and how it associates with a series of pertinent beliefs and opinions. Theoretical and practical implications 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)0.001
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 score0.481
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.049
GPT teacher head0.482
Teacher spread0.432 · 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
Published2025
Admission routes3
Has abstractyes

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