Measuring and reducing implicit prejudice against Black women and people with intersectional identities.
Published In: Social & Personality Psychology Compass, 2024, v. 18, n. 7. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Phills, Curtis E. 3 of 3
Abstract
This paper addresses a critical gap in measuring and reducing implicit prejudice: biases against Black women and people with intersectional identities. Though social psychologists have published many methods to measure and interventions to reduce implicit prejudice against Black people, these methods often target biases against Black people or Black men rather than Black women. Thus, these methods may leave Black women out because the mental representations of Black women and Black men differ and the mental representation of Black people is more similar to the mental representation of Black men than Black women. This paper advocates for an intersectional approach to measuring and reducing implicit prejudice that accounts for the unique prejudices faced by Black women. Specifically, this paper argues that researchers should tailor their methods to account for how the mental representations of Black women and Black men differ including differences in stereotypic content and ambivalence. The paper concludes by acknowledging the difficulties related to developing long‐lasting interventions, the need to move beyond reducing implicit prejudice, and the value of studying the men and women of additional racial and ethnic groups and other intersections like sexual orientation and socioeconomic status. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Social & Personality Psychology Compass. 2024/07, Vol. 18, Issue 7, p1
- Document Type:Article
- Subject Area:Psychology
- Publication Date:2024
- ISSN:1751-9004
- DOI:10.1111/spc3.12981
- Accession Number:178649317
- Copyright Statement:Copyright of Social & Personality Psychology Compass is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.