The visibility of social class from facial cues.
Bibliographic record
Abstract
Social class meaningfully impacts individuals' life outcomes and daily interactions, and the mere perception of one's socioeconomic standing can have significant ramifications. To better understand how people infer others' social class, we therefore tested the legibility of class (operationalized as monetary income) from facial images, finding across 4 participant samples and 2 stimulus sets that perceivers categorized the faces of rich and poor targets significantly better than chance. Further investigation showed that perceivers categorize social class using minimal facial cues and employ a variety of stereotype-related impressions to make their judgments. Of these, attractiveness accurately cued higher social class in self-selected dating profile photos. However, only the stereotype that well-being positively relates to wealth served as a valid cue in neutral faces. Indeed, neutrally posed rich targets displayed more positive affect relative to poor targets and perceivers used this affective information to categorize their social class. Impressions of social class from these facial cues also influenced participants' evaluations of the targets' employability, demonstrating that face-based perceptions of social class may have important downstream consequences. (PsycINFO Database Record
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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".