Changing Perceptions of Beauty: A Surgeon's Perspective
Bibliographic record
Abstract
Beauty is a mystery that has been with us for ages. Scholars and scientists have investigated its roots and effects, and its presence is ubiquitous. Has the construct of beauty changed over time? Is our sense of beauty learned or innate? What IS beauty, and can we quantify it? A substantial amount of work supports a Darwinian theory of selection, which predicts a survival advantage based on physical attractiveness. However, there is evidence that certain perceptions of beauty change with time. Indeed, the recent globalization of modern society has wrought changes in our perceptions of beauty. Are patients electing cosmetic surgery procuring a survival advantage, or are they bypassing genetics and setting a new standard for beauty? As facial plastic surgeons, we must be poised to respond to this metamorphosis and understand its roots. Although there is some equivocation and debate about this elusive subject, it is our duty to stay abreast of the current dynamic to make sound judgments that are in the best interests of our patients.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".