Face-to-Face: An Investigation of Social Comparisons of Facial Attractiveness
Bibliographic record
Abstract
Exposure to a face triggers a series of judgments about the person to which the face belongs. Some of the most widely studied judgments humans make from faces are evaluative judgments of facial attractiveness. These judgments are made frequently, automatically, and have important implications for judgments of other important social characteristics like trustworthiness and personality. A relatively understudied set of facial attractiveness judgments that are made from faces are comparative judgments, or social comparisons, of facial attractiveness. Based on social comparison theory, and what is known about evaluative judgments of facial attractiveness, it is likely that social comparisons of facial attractiveness are also frequent and have important implications. Despite this, little is known about social comparisons of facial attractiveness. To address this gap in the literature, three multi-method studies were conducted in this dissertation. The first was a systematic review to synthesize the existing research that directly or indirectly addressed social comparisons of facial attractiveness. Studies Two and Three empirically investigated whether or not social comparisons of facial attractiveness have features of automaticity. Study Two used a think aloud methodology to investigate the mandatory nature of social comparisons of facial attractiveness, whereas Study Three experimentally investigated how quickly social comparisons of facial attractiveness can be made. Together, these studies revealed more about how social comparisons of facial attractiveness occur and have theoretical implications for future research on the automaticity of mental processes more broadly.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".