Does Emotional Expression Moderate Implicit Racial Bias? Examining Bias Following Smiling and Angry Primes
Why this work is in the frame
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Bibliographic record
Abstract
Given the pervasiveness of prejudice, researchers have become increasingly interested in examining racial bias at the intersection of race and other social and perceptual categories that have the potential to disrupt these negative attitudes. Across three studies, we examined whether the emotional expression of racial exemplars would moderate implicit racial bias. We found that racial bias on the Affect Misattribution Procedure only emerged in response to angry but not smiling Black male faces in comparison to White (Study 1) or White and Asian (Study 3) male faces with similar emotional expressions. Racial bias was also found toward Asian targets (Studies 2 and 3), but not only following angry primes. These findings suggest that negative stereotypes about Black men can create a contrast effect, making racial bias toward smiling faces less likely to be expressed in the presence of angry Black male faces.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it