The Accuracy and Bias of Interpersonal Perceptions in Intergroup Interactions
Bibliographic record
Abstract
Group membership can have a profound impact on perceptions of group characteristics; yet how group membership influences the accuracy of personality impressions for specific individuals remains unclear. In small groups, participants ( N = 519) formed impressions via naturalistic, dyadic interactions. We then investigated whether impressions of in-group members differed from out-group members based on participant’s ethnicity and acculturation (Euro-Canadian, Acculturated East Asian, or Semi-Acculturated East Asian). Impressions of in-group members were more distinctively accurate and individuated. Further, in-group members were viewed with greater distinctive assumed similarity in that perceivers used their own idiosyncratic traits more when forming impressions of in-group members. However, in-group members, despite being liked more, were viewed less socially desirable. Discussion focuses on cultural differences in impressions and implications for in-group favoritism, in-group self-anchoring, and the out-group homogeneity effect for North Americans and East Asians.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| 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 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".