JOURNAL ARTICLE

Should Bionormativity Be a Concern in Gamete Donation?

  • Published In: IJFAB: International Journal of Feminist Approaches to Bioethics, 2023, v. 16, n. 2. P. 138 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Schuman, Olivia 3 of 3

Abstract

This article examines the ethical debate surrounding the removal of donor anonymity in gamete donation, focusing on the tension between donor-conceived individuals' expressed desires to know their genetic origins and concerns about reinforcing harmful bionormative attitudes—norms that prioritize genetic relatedness as essential to family identity. It critically analyzes the "Illegitimacy View," notably defended by philosopher Inmaculada de Melo-Martín, which argues that the desire for genetic knowledge is grounded in biased assumptions and that removing anonymity may perpetuate bionormativity without effectively addressing its harms. The article challenges this view by highlighting the acute psychological harms donor-conceived individuals experience due to anonymity policies, the limited evidence that maintaining anonymity reduces bionormativity, and the importance of respecting donor-conceived individuals' preferences in policy decisions. Ultimately, it advocates a middle-ground position that supports removing donor anonymity to alleviate immediate harms while continuing broader societal efforts to critique and reduce bionormative pressures.

Additional Information

  • Source:IJFAB: International Journal of Feminist Approaches to Bioethics. 2023/10, Vol. 16, Issue 2, p138
  • Document Type:Article
  • Subject Area:Biology
  • Publication Date:2023
  • ISSN:1937-4585
  • DOI:10.3138/ijfab-2023-0009
  • Accession Number:173469316
  • Copyright Statement:Copyright of IJFAB: International Journal of Feminist Approaches to Bioethics is the property of University of Toronto Press 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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