Is what is beautiful good and still more accurately understood? A replication and extension of Lorenzo et al. (2010)
Bibliographic record
Abstract
Is what is beautiful good and more accurately understood? Lorenzo et al. (2010) explored this question and found that more attractive targets (as per consensus) were judged more positively and accurately. Perceivers’ specific (idiosyncratic) ratings of targets’ attractiveness were also related to more positive and accurate impressions, but the latter was only true for highly consensually attractive targets. With a larger sample ( N = 547), employing a round-robin study design, we aimed to replicate and extend these findings by (1) using a more reliable accuracy criterion, (2) using a direct measure of positive personality impressions, and (3) exploring attention as a potential mechanism of these links. We found that targets’ consensual attractiveness was not significantly related to the positivity or the accuracy of impressions. Replicating the original findings, idiosyncratic attractiveness was related to more positive impressions. The association between idiosyncratic attractiveness and accuracy was again dependent on consensual attractiveness, but here, idiosyncratic attractiveness was associated with lower accuracy for less consensually attractive targets. Perceivers’ attention helped explain these associations. These results partially replicate the original findings while also providing new insight: What is beautiful to the beholder is good but is less accurately understood if the target is consensually less attractive.
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".