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        Revisualising Intersectionality

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        Author(s)
        Haschemi Yekani, Elahe
        Nowicka, Magdalena
        Roxanne, Tiara
        Language
        English
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        Abstract
        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.
        URI
        https://library.oapen.org/handle/20.500.12657/53353
        Keywords
        Intersectionality; Visuality; Artistic Research; Difference; Visual Culture
        DOI
        10.1007/978-3-030-93209-1
        ISBN
        9783030932091, 9783030932091
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        Cham, 2022
        Imprint
        Palgrave Macmillan
        Classification
        Films, cinema
        Television
        Cognition and cognitive psychology
        Crime and criminology
        Gender studies, gender groups
        Pages
        132
        Rights
        http://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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