The Up‐ and Downside of Dual Identity: Stereotype Threat and Minority Performance
Bibliographic record
Abstract
Abstract Social identity and acculturation research mostly documents benefits of dual identity for immigrant minorities’ adaptation. Drawing on stereotype threat research, we argue that dual identity can be (1) beneficial in low‐threat contexts and (2) costly in high‐threat contexts. Two field experiments in schools induced stereotype threat by randomly assigning minority students (Study 1: N = 174, Study 2: N = 735) to stereotype threat (making ethnicity salient) versus control conditions before taking a test. We assessed dual identity as dual commitments to (combined) minority and majority cultures. In support of the predicted benefits of dual identity in low‐threat contexts, dual identifiers outperformed and had higher self‐esteem than did otherwise‐identified students in the control condition, while the advantage of dual identity disappeared in the threat condition (Study 1). In support of the predicted costs of a dual identity in high‐threat contexts, dual identifiers reported more anxiety (Study 1) and performed worse (Study 2) in the threat condition compared to the control condition. These experimental findings suggest that dual identities may either help or hinder minority performance depending on stereotype threat in academic contexts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".