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Description
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. 
Pages
132 pages
Collection
n.c
Parution
2022-03-11
Marque
Palgrave Macmillan
EAN papier
9783030932084
EAN PDF
9783030932091

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
13
Taille du fichier
2478 Ko
Prix
0,00 €
EAN EPUB
9783030932091

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
13
Taille du fichier
3113 Ko
Prix
0,00 €

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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