Not Always Black and White: The Effect of Race and Emotional Expression on Implicit Attitudes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Across three studies we examined people's implicit attitudes toward Black and White targets who differed systematically by emotional expression. In Study 1, both the race and the emotional expression of primes affected people's attitudes as measured by the Affect Misattribution Procedure (AMP; Payne, Cheng, Govorun, & Stewart, 2005). In Study 2, participants completed an Implicit Association Test (IAT; Greenwald, Nosek, & Banaji, 2003) containing smiling Black and neutral White faces. When categorizing by race, participants implicitly preferred neutral White, over smiling Black, faces. By contrast, when categorizing by emotional expression, participants showed an implicit preference for smiling Black faces. In Study 3, participants spontaneously categorized these faces by race or emotional expression. Implicit biases again reflected participants' social categorization, however the majority of non-Black participants spontaneously categorized by race. Taken together, these results suggest that how we categorize multiply categorizable others directly affects our spontaneous affective responses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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