Judging a Book by Its Cover: Children's Facial Trustworthiness as Judged by Strangers Predicts Their Real-World Trustworthiness and Peer Relationships
Bibliographic record
Abstract
This longitudinal research examined whether children's facial trustworthiness as judged by strangers can predict their real-world trustworthiness and peer acceptance. Adults (Study 1) and children (Study 2) judged the facial trustworthiness of 8- to 12-year-old children (N = 100) solely based on their photographs. The children's classmates were asked to report their real-world trustworthiness and peer acceptance. Children's facial trustworthiness reliably predicted these outcomes both initially when the photographs were taken, as well as 1 year later, and this effect was mediated by the initial ratings of real-world trustworthiness and peer acceptance. These results provide evidence for a long-lasting linkage between children's facial and real-world trustworthiness.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".