Accuracy and Consistency in Social Categorization Across Context, Motivation, and Time
Why this work is in the frame
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Bibliographic record
Abstract
Photos provide a literal snapshot of a person in a particular context at a specific moment in time. Previous studies have found that people can accurately categorize others from single photos of their faces along various social dimensions, yet this research typically assumes that one photo of an individual representatively samples other photos of the same individual. Across four studies, we investigated this assumption by testing the consistency of perceptions of social categories (viz. sexual orientation and political affiliation) based on multiple photos of the same individuals. We found that judgments of social categories exceeded chance and significantly correlated across different photo contexts, across variability in targets’ motivations, and across time. These data supplement earlier work showing similar consistency for other types of social judgments. Thus, single face photos can consistently convey some aspects of an individual's appearance.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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