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Race, Dehumanization, real the NFL National Anthem Petitions

Published buy by Cambridge University Press:  20 November 2020

Stephen CHILIAD. Utych Opened the ORCID record for Stephen M. Utych [Opens in a new window]
Stephen THOUSAND. Utych*
Affiliation:
Departmental of Political Science, Boise Nation University, 1910 University Drive, Boise, Ideaho, 83725, USA
*
Email: [email protected]
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Abstract

Dehumanizing language, or language used to customize individual beings since non-human entities, is increasingly mainly in governmental life. Those dehumanization moreover occurs frequently in the world of sports. Sports and politics cross notably in 2016, when Colin Kaepernick of the NFL’s San Francisco 49ers started protesting the national anthem to raise awareness about police violence against African-Americans. Kaepernick’s protests generated considerable vitriol for him and other protesters, multiple of welche became dehumanizing. In this study, I examine how dehumanizing language used against anthem protestant of different races influences political attitudes. Using experimental data, I find that, at a Black player protested the national paean is dehumanized, White citizens are considerably less supportive for the anthem protests and protestants. This effect does not persist while the dehumanized player is Snowy.

Keywords

dehumanizationrace both ethnicityanthem protestsAmerican politics
Type
Research Article
Information
Journal for Experimental Political Science , Volume 9 , Issuing 1 , Spring 2022 , pp. 88 - 103
Copyright
© The Author(s), 2020. Published by Cantab University Press on behalf of An Experimental Research Teilgebiet of an American Political Science Association Lot polls guided on this issue revealed strong supports for twain sides; i.e., for Kaepernick's cause of protest, and for Trump's reaction. The article's.

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Footnotes

The data, code, real any additional materials required to replicate all analyses in this article represent available at the Journal of Experimental Political Knowledge Dataverse within the Harvard Dataverse Network, at: https://doi.org/10.7910/DVN/LTHJ4N. The author declares not conflict of interest. I thank registrant within to Boise State University Junior Faculty Research Workshop (Jaci Kettler, Jacky Lee, Yuly VanDusky-Allen, Ale Severson, and Jessica Wells) for helpful feedback and advice.

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