How are emotions and beliefs expressed in legislative testimonies? An advocacy coalition approach.

  • Published In: Review of Policy Research, 2024, v. 41, n. 4. P. 587 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gabehart, Kayla M.; Fullerton, Allegra H.; Crawford, Anna M.; Weible, Christopher M. 3 of 3

Abstract

While emotions are an inherent component of the human experience that influence behavior, values, and beliefs, they have largely been left out of policy process studies theoretically and methodologically. Using the Advocacy Coalition Framework (ACF), with its focus on how individuals coalesce into coalitions around a set of common beliefs, we begin to situate emotions as a critical component of belief systems and discourse about public policies. This study analyzes legislative testimony from four policies debated during the 2021 Colorado Legislative Session using discourse analysis to identify the emotions and coalitional beliefs. We find that policy actors express emotions and beliefs similarly to other policy actors in the same coalition and differently from policy actors in the opposing coalition. We conclude this paper by discussing the theoretical and methodological contributions of including emotions in the ACF. The move to incorporate the analysis of emotional expressions, and hence the study of affect, into the ACF mirrors the ongoing incorporation of how people feel in politics and not just how they think. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Review of Policy Research. 2024/07, Vol. 41, Issue 4, p587
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2024
  • ISSN:1541-132X
  • DOI:10.1111/ropr.12562
  • Accession Number:178715221
  • Copyright Statement:Copyright of Review of Policy Research is the property of Wiley-Blackwell 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.