Racial identity profiles of Asian-White biracial young adults: Testing a theoretical model with cultural and psychological correlates.
Bibliographic record
Abstract
Although the biracial population is expected to grow at astonishing rates in the upcoming decades across North America, rigorous quantitative psychological research on biracial identity is currently scarce. Therefore, the purpose of the present study was to examine biracial identity profiles in a large sample of Asian-White biracial young adults (n=330, aged 18-30) living in the United States and Canada, as well as assess the interrelationships among biracial identity and psychological adjustment variables. Grounded in the expanded theoretical model of Multiracial Heritage Awareness and Personal Affiliation (M-HAPA:Choi-Misailidis, 2004) and its corresponding biracial identity measure, cluster analysis was conducted to evaluate participants’ ‘patterns’ or ’profiles’ of scores on biracial identity orientation subscales. Three unique biracial identity groups emerged: the Asian-White Integrated, the Asian Dominant, and the White Dominant groups. Between-groups differences on participants’ measures of cultural socialization, psychological distress, and internalized oppression were analyzed and compared. The Asian-White Integrated group reported more cultural socialization than the other 2 groups. Furthermore, Asian Dominant participants showed the highest levels of psychological distress, whereas White Dominant participants showed the highest levels of internalized oppression among all groups. The results lend empirical support to the study’s hypotheses and the M-HAPA model. Theoretical, conceptual, and methodological implications for future biracial identity research 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".