JOURNAL ARTICLE

Solidarity and Gender in Protest Novels: Bessie Head’s The Cardinals and Miriam Tlali’s Muriel at Metropolitan.

  • Published In: English in Africa, 2023, v. 50, n. 3. P. 31 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Nivesjö, Sanja 3 of 3

Abstract

This article analyses the critical role of gender within racial solidarity in antiapartheid protest writing by comparing Bessie Head’s The Cardinals and Miriam Tlali’s Muriel at Metropolitan. Both novels depict the protagonist’s experience of being the only Black woman at a white-collar workplace during apartheid. The workplace relationships explored within the novels illustrate the complex role of gender in forming alliances and nurturing solidarity. Although Tlali’s novel prioritises solidarity between Black people in the face of apartheid, areas of significant gender inequalities are present. Head’s The Cardinals, on the other hand, explicitly explores the fissures in Black anti-apartheid alliances created by gender inequalities. The juxtaposition of these two novels challenges a uniform understanding of Black anti-apartheid solidarity, while also elucidating depictions of the competing relationships of solidarity for Black working women in female-authored protest writing. By forcing the reader to consider the place of gender in relation to both collectives of solidarity and to anti-apartheid protest, the novels trigger a rethinking of what traditionally male-dominated protest writing was and could be. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:English in Africa. 2023/11, Vol. 50, Issue 3, p31
  • Document Type:Literary Criticism
  • Subject Area:History
  • Publication Date:2023
  • ISSN:0376-8902
  • DOI:10.4314/eia.v50i3.3
  • Accession Number:177127963
  • Copyright Statement:Copyright of English in Africa is the property of Institute for Study English in Africa and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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