Perceived discrimination, group identification, and life satisfaction among multiracial people: A test of the rejection-identification model.
Bibliographic record
Abstract
Like other racial minority groups, multiracial people face discrimination as a function of their racial identity, and this discrimination represents a threat to psychological well-being. Following the Rejection-Identification Model (RIM; Branscombe, Schmitt, & Harvey, 1999), we argue that perceived discrimination will encourage multiracial people to identify more strongly with other multiracials, and that multiracial identification, in turn, fosters psychological well-being. Thus, multiracial identification is conceptualized as a coping response that reduces the overall costs of discrimination on well-being. This study is the first to test the RIM in a sample of multiracial people. Multiracial participants' perceptions of discrimination were negatively related to life satisfaction. Consistent with the RIM, perceived discrimination was positively related to three aspects of multiracial group identification: stereotyping the self as similar to other multiracial people, perceiving people within the multiracial category as more homogenous, and expressing solidarity with the multiracial category. Self-stereotyping was the only aspect of group identification that mediated a positive relationship between perceived discrimination and life satisfaction, suggesting that multiracial identification's protective properties rest in the fact that it provides an collective identity where one "fits."
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".