An investigation of how gender shapes the appearance and judgment of apologetic faces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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