JOURNAL ARTICLE

Taking Ideas Seriously in Political Science: The Diffusion of Presidentialism in Latin America after Independence.

  • Published In: Political Science Quarterly (Oxford University Press / USA), 2023, v. 138, n. 2. P. 217 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Parsons, Craig; Garcé, Adolfo; Béland, Daniel 3 of 3

Abstract

The article examines the asymmetrical role of ideational theories—those emphasizing the causal importance of ideas—in political science, highlighting that while most scholars agree abstractly that "ideas matter," ideational approaches remain underutilized compared to nonideational theories. Using nineteenth-century Latin American constitutional design as a case study, it illustrates how ideas about governance, notably the diffusion and adaptation of the U.S. presidential model combined with Spanish colonial legacies, played a central role in shaping political institutions, yet political scientists often neglect these ideational factors in favor of materialist or rationalist explanations. The authors argue that common methodological and epistemological objections to studying ideas are overstated, as ideational and nonideational theories face similar empirical challenges, and that rigorous research requires symmetrical engagement with both. They call for more balanced, pluralistic debates that seriously incorporate ideational explanations to enhance the discipline's overall rigor and understanding of political outcomes.

Additional Information

  • Source:Political Science Quarterly (Oxford University Press / USA). 2023/06, Vol. 138, Issue 2, p217
  • Document Type:Article
  • Subject Area:Political Science
  • Publication Date:2023
  • ISSN:0032-3195
  • DOI:10.1093/psquar/qqad008
  • Accession Number:164984264
  • Copyright Statement:Copyright of Political Science Quarterly (Oxford University Press / USA) 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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