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Passing as resistance through a Goffmanian approach: Normalized, defensive, strategic, and instrumental passing when LGBTQ+ individuals encounter institutions.

  • Published In: Gender, Work & Organization, 2023, v. 30, n. 3. P. 862 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ozbilgin, Mustafa F.; Erbil, Cihat; Baykut, Sibel; Kamasak, Rifat 3 of 3

Abstract

Passing and coming out are two divergent individual strategies historically associated with the LGBTQ+ community as they struggle to fit in with normative expectations at work and in life. While coming out has gradually become more common in organizations and national contexts that offer safeguards for LGBTQ+ individuals, passing remains an option where no such measures are available. Drawing on interviews with working‐class LGBTQ+ individuals in a country with an adversarial context, that is, Turkey, we identify how varieties of passing, defined as acting and appearing to fit with the dominant sexual orientation and gender identity norms, are used as strategies of coping with institutional norms. Working‐class LGBTQ+ individuals are an important group to study as many draw their pride, power, and identity from their engagement with work and the labor market. Transcending the monolithic accounts of passing, we illustrate four variants of passing (i.e., normalized, defensive, strategic, and instrumental passing) that LGBTQ+ individuals deploy at work. Reflecting on the field study findings, we explicate how and why LGBTQ+ individuals choose to pass at work in each case. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Gender, Work & Organization. 2023/05, Vol. 30, Issue 3, p862
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:0968-6673
  • DOI:10.1111/gwao.12928
  • Accession Number:162972119
  • Copyright Statement:Copyright of Gender, Work & Organization is the property of Wiley-Blackwell 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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