An Integrative Framework of Intersectional Stereotyping: Merging the Lens Model and MOSAIC to Explain Variations in Black-Threat Stereotyping

Abstract

Academic Abstract

Black-threat stereotyping, or the tendency to disproportionately associate Black individuals with threat-related concepts, is an artifact of American psychology. Nonetheless, most of what psychologists know about this topic concerns how Black-threat stereotypes are applied to young, straight Black men. In the present analysis, we review existing research on intersectional Black-threat stereotyping: broadly, how Black-threat stereotyping depends on the multiple, intersecting social categories to which targets belong. Although the existing literature is at times contradictory, we argue that it can be well-explained by an Integrative Framework of Intersectional Stereotyping that merges the core assumptions of two recent theories of intersectional stereotyping: the lens model and MOSAIC. The central contribution of the proposed framework is that intersectional stereotypes may at times be retrieved from memory and at other times be dynamically generated. We conclude with a roadmap for future research in this consequential domain.

Public Abstract

In the United States, Black Americans are killed by police at over twice the rate of White Americans. There are many reasons for this racial disparity, not the least of which is that Black Americans are often stereotyped as threatening. In the present paper, we review what scientists currently know about these inaccurate perceptions, and we discuss the question of whether these perceptions depend on the multiple social groups to which Black Americans belong (for example, Black Americans’ age, gender, and sexual orientation groups). We argue that although research on this topic is messy, it can be “cleaned up” with the help of what we call an Integrative Framework of Intersectional Stereotyping. This framework merges insights from two recent theories of intersectional stereotyping: the lens model and MOSAIC.