The visibility of social class from facial cues.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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