Preservice teachers’ racialized emotion recognition, anger bias, and hostility attributions
By A. Halberstadt, Vanessa L. Castro, Qiao Chu, Fantasy T. Lozada, Calvin M Sims. Contemporary Educational Psychology, 2018
100 citations recorded by the source corpus.
Abstract Differential treatment of students by race is well documented, and potentially driven by implicit processes relating to racial prejudice. To better understand some of the pathways by which racial prejudice may be activated, we examined preservice teachers’ racialized perceptions specific to emotion. Forty preservice teachers identified the emotions expressed in 20 Black and White male and female faces in order to assess racialized emotion accuracy and anger bias; participants also judged hostility in videos of 4 Black and White boys’ misbehaviors with peers in school in order to assess racialized attributions. We conducted a series of multilevel models with assessments of Black and White faces (or boys) nested within preservice teachers. Results indicated that emotions in Black faces were less accurately recognized than emotions in White faces; Black faces were more likely to be seen as angry even when they were not, compared to White faces; and boys’ misbehaviors were perceived as more hostile than those of White boys. Together, these results consistently suggest that racialized emotion-related perceptions may enter the classroom with preservice teachers. Implications, as well as limitations that may be resolved in future studies, and extensions of these findings to other minority status populations are discussed.
Subfields: Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/f957826e85fdf331d8d39f6bcfa4aae61c091b9a
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Citations in the corpus
- Back to the Future: Achieving Health Equity Through Health Informatics and Digital Health (2019, background): “Moreover, racial and ethnic minority groups have been historically faced with negative stereotyping bias [73-76] and unjust criminal sentences [77,78], which unfortunately have been recently promulgated through present-day data discrimination via search engines [79] and machine learning algorithms [”