Impact of Duchenne and non-Duchenne smiles on perceived trustworthiness of Black and White faces: A Black perspective.
Bibliographic record
Abstract
In five experiments, we investigated how Black participants perceive Duchenne and non-Duchenne smiles on Black and White targets. Results consistently demonstrated that when assessing happiness, faces with Duchenne compared to non-Duchenne smiles were rated as happier on both Black and White targets. However, when assessing a more socially evaluative dimension, trustworthiness, perceptions of Black and White targets diverged. Whereas White targets with Duchenne compared to non-Duchenne smiles were rated as more trustworthy, ratings of Black targets with Duchenne and non-Duchenne smiles did not differ, with both appraised as highly trustworthy. Although the degree to which Black participants identified with their race did not moderate these effects, the perceived genuineness of targets did mediate the relationship. One reason why Duchenne compared to non-Duchenne smiles on White but not Black targets were perceived as more trustworthy is because Duchenne compared to non-Duchenne smiles on White but not Black targets were perceived as more genuine. A final study extended these findings by exploring the impact of target race and smile type on partner choice. In accordance with the results related to trustworthiness ratings, Black participants selected White partners with Duchenne compared to non-Duchenne smiles more often but did not differentiate in their choice of Black partners with Duchenne versus non-Duchenne smiles. These findings underscore the importance of investigating not only diverse targets but also diverse perceivers. Our results suggest that Black perceivers use facial cues differently when rating the trustworthiness of Black and White targets and that these perceptions have important downstream consequences. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".