Bibliographic record
Abstract
Building on prior empirical research documenting differences in perceived attractiveness across racialized groups, we investigate how race is associated with attractiveness ratings using a large-scale dataset of facial photographs drawn from widely used face databases. This project is informed by a systematic literature review, which found that White and mixed-race individuals are often rated as more attractive than individuals from other racialized groups. Additionally, prior work has highlighted the potential role of perceived masculinity and femininity as mediators in race–attractiveness associations. Importantly, the review also emphasized that the relationship between race and attractiveness may interact with gender. The aim of the present study is to systematically test these patterns using cross-classified multilevel modeling, accounting for the nested structure of ratings across raters, targets, and databases. By modeling both individual-level and group-level sources of variation, we aim to provide a more nuanced and generalizable understanding of how race and gender intersect in shaping perceptions of facial attractiveness. This study represents our second attempt at analyzing these face databases; we previously preregistered a simpler analytic approach using summary statistics for target pictures that did not include rater info and observation-level data (see preregistration AsPredicted #174481). In the present study, we build on that work by applying more rigorous cross-classified multilevel modeling to better account for the nested structure of the data. A copy of the previous preregistration (blinded for peer-review) is included as a pdf file under "study design".
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.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".