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 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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".