Trustworthy-Looking Face Meets Brown Eyes
Bibliographic record
Abstract
We tested whether eye color influences perception of trustworthiness. Facial photographs of 40 female and 40 male students were rated for perceived trustworthiness. Eye color had a significant effect, the brown-eyed faces being perceived as more trustworthy than the blue-eyed ones. Geometric morphometrics, however, revealed significant correlations between eye color and face shape. Thus, face shape likewise had a significant effect on perceived trustworthiness but only for male faces, the effect for female faces not being significant. To determine whether perception of trustworthiness was being influenced primarily by eye color or by face shape, we recolored the eyes on the same male facial photos and repeated the test procedure. Eye color now had no effect on perceived trustworthiness. We concluded that although the brown-eyed faces were perceived as more trustworthy than the blue-eyed ones, it was not brown eye color per se that caused the stronger perception of trustworthiness but rather the facial features associated with brown eyes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".