The Up‐ and Downside of Dual Identity: Stereotype Threat and Minority Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it