Consistent Facial Cues to Social Class Across Two Different Western Contexts
Bibliographic record
Abstract
ABSTRACT Individuals form impressions of others’ social‐class standing from nonverbal information, including facial appearance. Whether the facial cues relating to (perceptions of) social class generalize across different contexts and class measures (e.g., income and subjective status) remains unknown. We tested which facial cues relate to actual and perceived social class using multiple social‐class measures in two contexts: Canada (using contemporary lab‐based photos) and Iceland (using mid‐20th‐century yearbook photos). Results show that facial appearance reveals and predicts impressions of social class broadly (vs. only for specific measures). Greater facial Attractiveness (attractiveness/competence/health) and Positivity (affect/warmth) related to higher social‐class standing in both contexts, suggesting that social class influences facial appearance similarly in different environments. Attractiveness also primarily explained social‐class perceptions. Validity and utilization of other cues, however, differed between contexts, and we observed perception accuracy only for Canadian targets. These findings provide a more complete understanding of accuracy and bias in perceiving social class.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".