JOURNAL ARTICLE
Love as Enlightenment and Enlightenment as Love: Reading Feminist Hermeneutic of Reconstruction in Vanessa R Sasson's Yasodhara and the Buddha.
Published In: Feminist Theology: The Journal of the Britain & Ireland School of Feminist Theology, 2023, v. 31, n. 3. P. 353 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Wadhwa, Soni 3 of 3
Abstract
This article examines the intersection of feminist theology and Buddhist thought through Vanessa R. Sasson's debut novel *Yasodhara and the Buddha*, which reimagines the life of Buddha’s wife, Yasodhara, as a site of feminist consciousness intertwined with Buddhist philosophy. It highlights how the novel challenges conventional feminist aesthetics by portraying Yasodhara’s transformation from anger and resistance to a spiritual realization centered on attachment and detachment, reflecting Buddhist ideals of love and self-engagement rather than outward feminist resistance. The article situates this work within broader feminist theological debates on Buddhism, noting diverse scholarly perspectives ranging from critiques of misogyny to notions of genderlessness in Buddhist spirituality. Ultimately, it argues that Sasson’s fictional reconstruction offers a unique model for bridging secular and religious feminisms, expanding feminist discourse beyond Western frameworks by embracing spirituality, gender fluidity, and self-reflective agency.
Additional Information
- Source:Feminist Theology: The Journal of the Britain & Ireland School of Feminist Theology. 2023/05, Vol. 31, Issue 3, p353
- Document Type:Article
- Subject Area:History
- Publication Date:2023
- ISSN:0966-7350
- DOI:10.1177/09667350231163311
- Accession Number:163454085
- 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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