Facial Features Influence the Categorization of Female Sexual Orientation
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
Social categorization is a rapid and automatic process, and people rely on various facial cues to accurately categorize each other into social groups. Recently, studies have demonstrated that people integrate different cues to arrive at accurate impressions of others' sexual orientations. The amount of perceptual information available to perceivers could affect these categorizations, however. Here, we found that, as visual information decreased from full faces to internal facial features to just pairs of eyes, so did the accuracy of judging women's sexual orientation. Yet and still, accuracy remained significantly greater than chance across all conditions. More important, however, participants' response bias varied significantly depending on the facial feature judged. Perceivers were significantly more likely to consider that a target may be lesbian as they viewed less of the faces. Thus, although facial features may be continuously integrated in person construal, they can differentially affect how people see each other.
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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) | 0.006 | 0.001 |
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