Bibliographic record
Abstract
While there has been much emphasis on the objective properties of beautiful faces, some theories of physical attractiveness implicate norm-based coding of faces and experience-dependent preferences (e.g., Langlois & Roggman, 1990 ; Rhodes, Jeffery, Watson, Clifford, & Nakayama, 2003 ). This study further explored experiential influences by correlating a persons standing height with his/her ideal vertical location of the internal features in computerized faces. Taller raters created faces with larger ratios of forehead height to chin height–resulting in a larger forehead and a smaller chin, presumably caused by their biased exposure to faces from above eye level. Faces produced by shorter raters had a smaller forehead and a larger chin. The moderate correlation was maintained after controlling for age and gender (i.e., semipartial r = .41; N = 39), and rater height alone explained 24% of the variance in preferred location of the internal facial features. These results point to individual differences in perceptions of attractiveness, accounted for to some degree by the facial proportions encountered in everyday interactions.
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.
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 | 0.000 |
| 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".