Social Categories Alone Are Insufficient to Elicit an In-Group Advantage in Perceptions of Within-Person Variability
Why this work is in the frame
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Bibliographic record
Abstract
Within-person variability affects identity perception of other-race faces more than own-race faces; when participants sort images into piles representing different identities, they sort photographs of two other-race identities into more piles than two own-race identities. These results have been interpreted in terms of perceptual expertise, such that lack of experience with other-race faces leads to reduced ability to extract identity-relevant information across images. However, an alternative explanation is that sociocognitive factors (e.g., cognitive disregard for out-group faces) lead to differences in the number of perceived identities. Here, we examined whether social factors alone elicit an in-group advantage in perceptions of within-person variability. Caucasian participants sorted 40 photographs of two unfamiliar Caucasian identities (20 photographs/model) into piles based on the number of identities they believed were present. Half of the participants were told that the images were of students attending their university (in-group), whereas half were told that the images were of students attending a rival university (out-group). Participants sorted the photographs into a comparable number of identities for in- and out-group faces. This lack of an in-group advantage suggests that sociocognitive factors alone cannot account for differences in the number of perceived identities across faces from two categories.
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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 | 0.001 |
| 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 | 0.001 |
| 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 it