Face gender and emotion expression: Are angry women more like men?
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
Certain features of facial appearance perceptually resemble expressive cues related to facial displays of emotion. We hypothesized that because expressive markers of anger (such as lowered eyebrows) overlap with perceptual markers of male sex, perceivers would identify androgynous angry faces as more likely to be a man than a woman (Study 1) and would be slower to classify an angry woman as a woman than an angry man as a man (Study 2). Conversely, we hypothesized that because perceptual features of fear (raised eyebrows) and happiness (a rounded smiling face) overlap with female sex markers, perceivers would be more likely to identify an androgynous face showing these emotions as a woman than as a man (Study 1) and would be slower to identify happy and fearful men as men than happy and fearful women as women (Study 2). The results of the two studies showed that happiness and fear expressions bias sex discrimination toward the female, whereas anger expressions bias sex perception toward the male.
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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) | 0.001 | 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 it