What Is Beautiful Is Good and More Accurately Understood
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
Beautiful people are seen more positively than others, but are they also seen more accurately? In a round-robin design in which previously unacquainted individuals met for 3 min, results were consistent with the "beautiful is good" stereotype: More physically attractive individuals were viewed with greater normative accuracy; that is, they were viewed more in line with the highly desirable normative profile. Notably, more physically attractive targets were viewed more in line with their unique self-reported personality traits, that is, with greater distinctive accuracy. Further analyses revealed that both positivity and accuracy were to some extent in the eye of the beholder: Perceivers' idiosyncratic impressions of a target's attractiveness were also positively related to the positivity and accuracy of impressions. Overall, people do judge a book by its cover, but a beautiful cover prompts a closer reading, leading more physically attractive people to be seen both more positively and more accurately.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 it