What influences judgments of physical attractiveness? A comprehensive perspective with implications for mental health
Bibliographic record
Abstract
Judgments of physical attractiveness are based on appearance but are influenced by and influence more than just physical features of the face and body (e.g. clothing and personality traits). This is explored in a selective review of previous research, plus new analyses of data from three previously published studies: the Boston Couples Study, the Multiple Identities Questionnaire, and the Intimate Relationships Across Cultures Study, with implications for mental health. Self-ratings of attractiveness are inflated by self-esteem and confidence in self-halo effects. Partner-ratings are inflated by love and relationship satisfaction in partner-halo effects. Positive responses from others influence attractiveness-enhancing cycles, while negative responses influence attractiveness-deprecating cycles, with impacts on well-being. These influences are represented in a comprehensive Attractiveness Halo Model, which identifies Ten Components of Attractiveness that are inter-related, including physical, emotional, sexual, sensory, intellectual, behavioural, observer, situation, reciprocity, and time. Aspects of the model are supported by analyses of the three studies, generalising comprehensive attractiveness halo effects across time, identities, cultures, and relationship types.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".