History and Current Concepts in the Analysis of Facial Attractiveness
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
BACKGROUND: Facial attractiveness research has yielded many discoveries in the past 30 years, and facial cosmetic, plastic, and reconstructive surgeons should have a thorough understanding of these findings. Many of the recent studies were conducted by social, developmental, cognitive, and evolutionary psychologists, and although the findings have been published in the psychology literature, they have not been presented in a comprehensive manner appropriate to surgeons. METHODS: The author reviews the findings of facial attractiveness research from antiquity to the present day and highlights and analyzes important concepts necessary for a thorough understanding of facial attractiveness. RESULTS: Four important cues emerge as being the most important determinants of attractiveness: averageness (prototypicality), sexual dimorphism, youthfulness, and symmetry. CONCLUSIONS: A surgeon planning facial cosmetic, plastic, or reconstructive surgery can potentially gain both profound insight and better quality surgical results by appreciating these findings.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it