Predicting Intergroup Bias: The Interactive Effects of Implicit Theory and Social Identity
Bibliographic record
Abstract
This research sought to integrate the implicit theory approach and the social identity approach to understanding biases in intergroup judgment. The authors hypothesized that a belief in fixed human character would be associated with negative bias and prejudice against a maligned group regardless of the perceiver's social identity. By contrast, a belief in malleable human character would allow the perceiver's social identity to guide intergroup perception, such that a common ingroup identity that includes the maligned group would be associated with less negative bias and prejudice against the maligned group than would an exclusive identity. To test these hypotheses, a correlational study was conducted in the context of the Hong Kong 1997 political transition to examine Hong Kong Chinese's perceptions of Chinese Mainlanders, and an experimental study was conducted in the United States to examine Asian Americans' perception of African Americans. Results from both studies supported the authors' predictions.
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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 | 0.002 | 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 | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".