JOURNAL ARTICLE

Crypts of Memories and Law's Desire. Modernist Reconfigurations of Literary and Legal Traditions.

  • Published In: Pólemos (2035-5262), 2026, v. 20, n. 1. P. 91 1 of 3

  • Database: Humanities Source Ultimate 2 of 3

  • Authored By: Costantini, Cristina 3 of 3

Abstract

The essay explores the profound impact of modernist revisions of time, memory, and narrative in the legal domain. By tracing the fragmentation of linear chronologies and the destabilization of historical certainties introduced by modernist literature (from Virginia Woolf to T.S. Eliot), it reveals how the epistemology of tradition was reimagined as a dynamic interplay between pastness and presence, permanence and loss. The investigation highlights how these shifts not only influenced literary forms, but also fundamentally reshaped both, methodologically, the ways in which legal traditions are conceptualized in contemporary comparative discourse and, substantively, the formation of American legal consciousness. As for the first aspect, the analysis focuses on the seminal works of Patrick Glenn and Harold J. Berman. With respect to the second, a critical reading is offered of Holmes's and Cardozo's jurisprudence as a formative source for the internal rearticulation of the American common law tradition. Particular emphasis is placed on the rediscovery of dissenting opinions as privileged sites of active memory and counter-narrative, embodying the tension between stability and transformation. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Pólemos (2035-5262). 2026/04, Vol. 20, Issue 1, p91
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2026
  • ISSN:20355262
  • DOI:10.1515/pol-2026-2003
  • Accession Number:192932276
  • Copyright Statement:Copyright of Pólemos (2035-5262) is the property of De Gruyter 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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