The social identity and psychology of mixed-race individuals: An international study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".