An investigation of how gender shapes the appearance and judgment of apologetic faces
Bibliographic record
Abstract
Do people have mental representations of what apologetic faces look like? Do representations differ by gender? We used reverse correlation to (a) generate images that approximate mental representations of apologetic faces, (b) determine whether these images are rated highly on apology-related characteristics, and (c) see if ratings differ by gender of the image generator, target face, and/or image rater. Faces generated from male and female base faces to look apologetic were rated as more apologetic, remorseful, and sad than the base face, demonstrating these mental representations can be approximated using reverse correlation. Findings suggest visually represented apologies express multiple apology-related characteristics. Study 2 revealed the visual templates of faces generated by the gender ingroup appeared more apologetic than those of the gender outgroup; women-generated female faces were most apologetic, and men-generated female faces were least apologetic. Findings highlight gender differences in mental representation, but not perception, of female apologetic faces.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".