Consensus in social judgments of faces across world regions is driven by effects of distinctiveness on perceptions of prosociality, rather than effects of masculinity
Bibliographic record
Abstract
Social judgments of faces influence important social outcomes. Although many researchers have argued that facial masculinity plays a key role in perceptions of prosociality and dominance, whether these effects are consistent among people from different world regions is highly contentious. Consequently, we investigated possible relationships between masculinity and face ratings made by 11,484 participants from eleven world regions (Africa, Asia, Australia and New Zealand, Central America and Mexico, Eastern Europe, Middle East, Scandinavia, South America, United Kingdom, United States and Canada, Western Europe). Surprisingly, masculinity did not significantly predict perceived prosociality or dominance in any regions. By contrast, facial distinctiveness (i.e., atypicality) was significantly and negatively correlated with prosocial perceptions in all regions. Collectively, our results suggest that consensus in social judgments of faces among people from different world regions is driven by the effects of distinctiveness on prosocial perceptions (i.e., an “anomalous-is-bad” stereotype), rather than the effects of masculinity. This research was supported by ESRC grant ES/X000249/1 awarded to BCJ and University of Strathclyde Global Research Awards to KL and JD. For the purpose of Open Access, the authors have applied a Creative Commons Attribution (CC BY) to any Author Accepted Manuscript (AAM) version arising from this submission.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".