Are Social Categories Alone Sufficient to Elicit an In-Group Advantage in Perceptions of Within-Person Variability?
Bibliographic record
Abstract
Several studies (e.g., Bernstein et al., 2007) have demonstrated that social categorization in the absence of physical differences is sufficient to elicit recognition biases mimicking the other-race effect, thus suggesting that in-group biases alone underlie race-based deficits in face processing. If this is the case, then social categories alone should elicit an in-group advantage in another task that shows a strong benefit for own-race faces: a sorting task in which participants are asked to recognize the same identity across superficial changes (e.g., hairstyle, expression). Laurence et al. (2015) recently reported that when asked to sort images into piles representing different identities, participants sort photographs of two other-race identities into more piles than two own-race identities. In the present study, we examined whether this finding would replicate for faces that differed only in terms of social category. Caucasian participants (n = 48) were shown 40 photographs of two unfamiliar Caucasian identities (20 photographs/model) and asked to sort them into piles based on the number of identities they believed were present. Half of the participants were told that the faces were those of students currently attending their private university (a social in-group), whereas half were told that the faces were those of students currently attending a public university located out of province (a social out-group). Despite indicating that they strongly identified with their university affiliation, participants sorted the photographs into a comparable number of identities for in-group (M = 9.00, median = 8.50, range = 2-20) and out-group (M = 9.87, median = 9.50, range = 2-21) faces, p > .50, thus showing no in-group advantage in tasks examining perceptions of within-person variability. These results suggest that social categorical models of the other-race effect have limited explanatory power, as they cannot account for race-based biases in tasks outside of recognition memory. Meeting abstract presented at VSS 2016
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".