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The thick skin bias
Dissertation Abstracts International: Section B: The Sciences and Engineering ; 83(4-B):No Pagination Specified, 2022.
Article in English | APA PsycInfo | ID: covidwho-1716901
ABSTRACT
I propose a novel explanation for many of the social class disparities documented across the social and behavioral sciences the "thick skin bias." Across 33 studies, I show that people of lower socioeconomic status (SES) are systematically perceived to be less harmed by negative events than higher-SES people, even when this is patently false. In Chapter 1, I provide initial demonstrations of this thick skin bias, rule out multiple alternative explanations, and show that the bias extends to judgments about children as young as five and to judgments made by several professional populations and a nationally representative sample of the U.S. In Chapter 2, I investigate one mechanism underlying the thick skin bias an overgeneralization of the sometimes accurate intuition that perceptions adapt to prior levels of exposure (as in psychophysical adaptation). Chapter 3 extends the thick skin bias to the context of gender-based violence, and Chapter 4 extends it to the context of the COVID-19 pandemic. Chapter 4 also tests the efficacy of providing people with an informational prime as a method of reducing the thick skin bias information reduces, but does not fully reverse, the perception that people in poverty are less harmed by negative events. A brief conclusion charts future directions for research on the causes and consequences of and potential cures for the thick skin bias. Taken together, the work in this dissertation offers a new direction for research on social class stereotypes and provides a new perspective on how biased beliefs about people in poverty justify, perpetuate, and exacerbate economic inequality. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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Collection: Databases of international organizations Database: APA PsycInfo Language: English Journal: Dissertation Abstracts International: Section B: The Sciences and Engineering Year: 2022 Document Type: Article

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Collection: Databases of international organizations Database: APA PsycInfo Language: English Journal: Dissertation Abstracts International: Section B: The Sciences and Engineering Year: 2022 Document Type: Article