Person perception across group boundaries: a dynamic model of perception across race and gender lines
Bibliographic record
Abstract
People form impressions of others from their faces, inferring character traits (e.g., friendly) along two broad, influential dimensions: Warmth and Competence. Although these two dimensions are presumed to be independent, research has yet to examine the generalizability of this model to cross-group impressions, despite extant evidence that Warmth and Competence are not independent for outgroup targets. This thesis explores this possibility by testing models of person perception for own-group and other-group perceptions, implementing confirmatory factor analysis in a structural equation modeling framework, and analyzing the underlying trait space using representational similarity analysis. I fit 402,473 ratings of 873 unique faces from 5,040 participants on 14 trait impressions to own-group and other-group models, exploring whether perceptions across race and gender are more unidimensional. Results indicate that current models of face perception fit poorly and are not universal as presumed: the space of trait impressions varies depending on targets’ race and gender. Keywords: person perception, impression formation, face perception, intergroup processes, social cognition
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".