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Revisualising Intersectionality

Erschienen am 12.03.2022, 1. Auflage 2022
32,09 €
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Bibliografische Daten
ISBN/EAN: 9783030932084
Sprache: Englisch
Umfang: xi, 132 S., 10 farbige Illustr., 132 p. 10 illus.
Einband: gebundenes Buch

Beschreibung

Revisualising Intersectionality offers transdisciplinary interrogations of the supposed visual evidentiality of categories of human similarity and difference. This open-access book incorporates insights from social and cognitive science as well as psychology and philosophy to explain how we visually perceive physical differences and how cognition is fallible, processual, and dependent on who is looking in a specific context. Revisualising Intersectionality also puts into conversation visual culture studies and artistic research with approaches such as gender, queer, and trans studies as well as postcolonial and decolonial theory to complicate simplified notions of identity politics and cultural representation. The book proposes a revision of intersectionality research to challenge the predominance of categories of visible difference such as race and gender as analytical lenses.

Produktsicherheitsverordnung

Hersteller:
Springer Verlag GmbH
juergen.hartmann@springer.com
Tiergartenstr. 17
DE 69121 Heidelberg

Autorenportrait

Elahe Haschemi Yekani is Professor of English and American Literature and Culture with a Focus on Postcolonial Studies at the Department of English and American Studies at Humboldt-Universität zu Berlin. Haschemi Yekani is the author of Familial Feeling and The Privilege of Crisis. Magdalena Nowicka is a sociologist and Professor of Migration and Transnationalism at the Institute of Social Sciences at Humboldt-Universität zu Berlin and Head of the Department Integration at DeZIM e.V. - German Center for Integration and Migration Research in Berlin. Tiara Roxanne, (PhD) is an Indigenous cyberfeminist, scholar and artist based in Berlin. Her research and artistic practice investigates the encounter between the Indigenous Body and AI by interrogating colonial structures embedded within machine learning systems.

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