Registered report: Social face evaluation: ethnicity-specific differences in the judgement of trustworthiness of faces and facial parts
Bibliographic record
Abstract
Social face evaluation is a common and consequential element of everyday life based on the judgement of trustworthiness. However, the particular facial regions that guide such trustworthiness judgements are largely unknown. It is also unclear whether different facial regions are consistently utilized to guide judgments for different ethnic groups, and whether previous exposure to specific ethnicities in one's social environment has an influence on trustworthiness judgements made from faces or facial regions. This registered report addressed these questions through a global online survey study that recruited Asian, Black, Latino, and White raters (N = 4580). Raters were shown full faces and specific parts of the face for an ethnically diverse, sex-balanced set of 32 targets and rated targets' trustworthiness. Multilevel modelling showed that in forming trustworthiness judgements, raters relied most strongly on the eyes (with no substantial information loss vis-à-vis full faces). Corroborating ingroup-outgroup effects, raters rated faces and facial parts of targets with whom they shared their ethnicity, sex, or eye color as significantly more trustworthy. Exposure to ethnic groups in raters' social environment predicted trustworthiness ratings of other ethnic groups in nuanced ways. That is, raters from the ambient ethnic majority provided slightly higher trustworthiness ratings for stimuli of their own ethnicity compared to minority ethnicities. In contrast, raters from an ambient ethnic minority (e.g., immigrants) provided substantially lower trustworthiness ratings for stimuli of the ethnic majority. Taken together, the current study provides a new window into the psychological processes underlying social face evaluation and its cultural generalizability. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 7 January 2022. The protocol, as accepted by the journal, can be found at: https://doi.org/10.6084/m9.figshare.18319244 .
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.074 | 0.021 |
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".