Posing while black: The impact of race and expansive poses on trait attributions, professional evaluations, and interpersonal relations.
Bibliographic record
Abstract
A large literature on nonverbal behavior demonstrates that information from body cues can inform our impressions of others. This work, however, has largely focused on perceptions of White targets. The current experiments extend this research by investigating the impact of body poses on trait attributions, professional evaluations, and interpersonal relations for both White and Black targets. In four studies, participants were presented with images of White and Black targets with expansive and constrictive poses. Not surprisingly, Experiment 1 revealed that expansive relative to constrictive poses increased perceptions of dominance for targets of both races. Furthermore, for White and Black targets, perceptions of dominance from expansive poses were mediated by greater attributions of competence. For Black but not White targets, however, perceptions of dominance from expansive poses were mediated by greater attributions of aggression. Three additional experiments examined the influence of poses on evaluations in professional and interpersonal contexts. Experiment 2 indicated that expansive compared to constrictive poses led to greater expectations of professional success for White than Black targets. Experiments 3 and 4 demonstrated that expansive compared to constrictive poses led to a greater willingness to interact in an interpersonal setting with White but not Black targets. Attributions of aggression related to expansive poses by Black targets reduced the likelihood that they were chosen as interaction partners. The implications of these findings for understanding body perception and race relations are discussed. (PsycInfo Database Record (c) 2022 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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".