JOURNAL ARTICLE

Jueces en tensión: la Corte Suprema argentina (2003-2023) y la Corte Constitucional colombiana (2002-2023) frente a la polarizaciónJudges under stress: The Argentine Supreme Court (2003-2023) and the Colombian Constitutional Court (2002-2023) amid polarization

  • Published In: International Journal of Constitutional Law, 2024, v. 22, n. 5. P. 1424 1 of 3

  • Database: Legal Source 2 of 3

  • Authored By: Botero, Sandra; Holgado, Benjamín García 3 of 3

Abstract

This article examines how the Supreme Court of Argentina (2003–2023) and the Constitutional Court of Colombia (2002–2023) responded to periods of heightened political polarization. It finds that both courts acted strategically and maintained moderate stances, issuing rulings both favorable and adverse to their respective executives without permanent alignment to any political faction. The courts' relative independence is attributed to their judicial selection mechanisms and the distribution of power within their legislatures, which prevented domination by a single political force and internal ideological polarization. The study highlights that, unlike the highly polarized and politicized U.S. Supreme Court, these Latin American courts have so far avoided becoming tools of partisan conflict, balancing deference and resistance even during crises such as the COVID-19 pandemic. The article suggests ongoing challenges ahead as new administrations with ambitious reform agendas engage with these courts.

Additional Information

  • Source:International Journal of Constitutional Law. 2024/12, Vol. 22, Issue 5, p1424
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2024
  • ISSN:14742640
  • DOI:10.1093/icon/moae076
  • Accession Number:186054556
  • Copyright Statement:Copyright of International Journal of Constitutional Law is the property of Oxford University Press / USA 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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