JOURNAL ARTICLE

Theology, Gender, and Me: Insider Auto-Ethnographic Research Method and Its Impact in Trans-Related Theological Research.

  • Published In: Feminist Theology: The Journal of the Britain & Ireland School of Feminist Theology, 2024, v. 32, n. 3. P. 256 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Claire-Young, Alex 3 of 3

Abstract

This article examines the use of autoethnographic and interview-based insider research methods with trans and non-binary Christians, highlighting how these approaches facilitate proclamation, attention, dialogue, justice, and theological revelation. Drawing on doctoral research involving interviews with ten diverse trans and non-binary Christian participants and the author's own autoethnographic reflections as a transmasculine genderqueer Christian minister, the study employs a grounded paradigm attentive to power dynamics, multivocality, and intersectionality. It emphasizes the importance of centering trans voices in theological discourse, critiques normative frameworks, and explores embodiment as central to understanding incarnation and identity beyond binary categories. The article also reflects on the challenges and potentials of queer and feminist theories in this context, advocating for authentic, embodied theological dialogue that acknowledges complexity and diversity within trans Christian experiences.

Additional Information

  • Source:Feminist Theology: The Journal of the Britain & Ireland School of Feminist Theology. 2024/05, Vol. 32, Issue 3, p256
  • Document Type:Article
  • Subject Area:Religion and Philosophy
  • Publication Date:2024
  • ISSN:0966-7350
  • DOI:10.1177/09667350241233572
  • Accession Number:176694283
  • Copyright Statement:Copyright of Feminist Theology: The Journal of the Britain & Ireland School of Feminist Theology is the property of Sage Publications Inc. 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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