Bibliographic record
Abstract
BACKGROUND: Research over the past 20 years has shown that judgments of facial attractiveness are universal; people from all cultures and backgrounds rank and rate faces for attractiveness the same. As such a model for objectively rating facial attractiveness is theoretically plausible, if designed, it would have many uses, including outcomes analysis in plastic surgery of the face. The authors tested a schematic facial composite/prototype mathematical model (the phi mask created by Dr. Stephen Marquardt) as a method for measuring facial attractiveness in an objective manner. METHODS: Thirty-seven male and 35 female faces of 18- to 30-year-old whites of European extraction were rated, as were 31 composite faces of each sex using both Internet and direct survey judges. The faces were tested against the phi mask model analyzing deviations of facial anthropometric points from corresponding phi mask nodal points using equivalent weightings, and weightings arrived at by way of multiple linear regression. RESULTS: The deviation from the phi mask significantly correlates with attractiveness, explaining from 25 to 75 percent of the variance in attractiveness judgments, depending on the methodology used. CONCLUSIONS: The phi mask model supports averageness or prototypicality of the face as being the major component of the facial attractiveness gestalt and is a first step in producing an objective system for measuring facial attractiveness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".