“You are too ethnic, you are too national”: Dual identity denial and dual identification
Bibliographic record
Abstract
Ethnic minorities tend to develop dual identities and therefore can face identity denials from two groups. We examine in two studies the relation between dual identity and experiences of dual identity denial as misgivings or a manifested mistrust of one’s group membership from both majority and minority group members. Based on identity integration and threat literature, identity denial represents a threat to dual identity which means that stronger dual identity denial can be expected to be associated with lower dual identity (a negative association). In contrast, based on identity enactment literature, stronger expression of one’s dual identity can be expected to elicit stronger identity denial (a positive association). These two contrasting hypotheses were examined in two studies (Study 1 = 474; Study 2 = 820) among ethnic minorities in the Netherlands. The results from both studies offer greater support for the identity enactment model and illustrate the complexities associated with having a dual identity.
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How this classification was reachedexpand
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".