Beyond categories: Perceiving sexual attraction from faces
Bibliographic record
Abstract
Although people can categorize others' sexual orientation (e.g., gay/lesbian vs. straight) from their facial appearance, not everyone defines their sexual orientation categorically. Indeed, many individuals within the same sexual orientation category experience different degrees of own- and other-gender attraction. Moving beyond sexual orientation categories, we found that perceivers' judgments of individuals' sexual attraction correlated with those individuals' self-reported degrees of attraction to women and men. Similar to past work on sexual orientation categories, facial affect cued sexual attraction in men whereas gender typicality cued sexual attraction in women. Moreover, asking participants to categorize the targets as 'not straight' versus 'straight' revealed a linear pattern distinct from the discrete category thresholds typical of other social groups (e.g., race). Facial appearance thus reveals nuances in sexual attraction that support sexual orientation categorizations. These findings refine understanding of social categorization more broadly.
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How this classification was reachedexpand
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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 teacher head, 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".