Bibliographic record
Abstract
Some evidence suggests that lay persons are able to perceive sexual orientation from face stimuli above the chance level. A morphometric study of 390 heterosexual and homosexual Canadian people of both sexes reported that facial structure differed depending on the sexual orientation. Gay and heterosexual men differed on three metrics as the most robust multivariate predictors, and lesbian and heterosexual women differed on four metrics. A later study verified the perceptual validity of these multivariate predictors using artificial three-dimensional face models created by manipulating the key parameters. Nevertheless, there is evidence of important processing differences between the perception of real faces and the perception of artificial computer-generated faces. The present study which composed of two experiments tested the robustness of the previous findings and extended the research by experimentally manipulating the facial features in face models created from photographs of real people. Participants of the Experiment 1 achieved an overall accuracy (0.67) significantly above the chance level (0.50) in a binary hetero/homosexual judgement task, with some important differences between male and female judgements. On the other hand, results of the Experiment 2 showed that participants rated the apparent sexual orientation of series of face models created from natural photographs as a continuous linear function of the multivariate predictors. Theoretical implications are discussed.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".