City Slicker or Country Bumpkin?—Distinguishing Urban and Rural Residents From Subtle Facial Cues
Bibliographic record
Abstract
ABSTRACT Stereotypes characterize urban and rural residents as differing in traits, values and social outcomes. Here, we examined how people's stereotypes about urban and rural residents differ, testing their validity using a lens model. Results showed that participants detected whether people resided in urban or rural areas from photos across three nations: the United States, Canada and Japan. North American and Japanese participants shared similar stereotypes of urban residents, seeing them as more likely to appear competent, dominant and positive in affect than rural residents. Yet, whereas inferences of competence, dominance, attractiveness and perceived age explained accurate urban–rural judgements in North America, only perceived age explained accurate urban–rural judgements in Japan. Together, these findings illuminate how both diagnostic and misleading social trait expectations account for differences in social perception across cultures, contributing to an ecologically functional account of social perception.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".