JOURNAL ARTICLE
The Magnitude of Triggering Events and the Nonlinear Dynamics of Ethnic and Religious Upheavals.
Published In: Conflict Resolution Quarterly, 2025, v. 43, n. 1. P. 41 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Schulte, Felix; Trinn, Christoph 3 of 3
Abstract
Ethnic and religious conflicts often resemble the proverbial powder keg, characterized by sudden eruptions of conflictive mass behavior in the form of protests or riots. Typically, such escalation episodes are ignited by highly disruptive events: Point‐like triggers at a specific juncture of time and space that facilitate and precipitate collective action. Despite rich anecdotal evidence, triggering events as proximate causes of identity conflicts remain underexplored in empirical research. Challenging the prevailing linear perspective, we argue that the magnitude of a triggering event holds little significance in explaining the intensity of the ensuing ethnic or religious upheavals. Using a new, human‐coded dataset of 642 escalation episodes that details the specific triggering events and the intensity of each wave of protest or riot, we test our hypothesis by applying Bayes Factors and Bayesian regression models. Our findings provide robust empirical evidence for our hypothesis: Major precipitating events are no more likely to trigger high‐intensity upheavals than minor events. These results have significant implications for the generalizability of studies that focus on specific types of triggering events, such as repression and "focal days", and the overall potential of forecasting the escalation of intrastate conflict. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Conflict Resolution Quarterly. 2025/09, Vol. 43, Issue 1, p41
- Document Type:Article
- Subject Area:History
- Publication Date:2025
- ISSN:1536-5581
- DOI:10.1002/crq.21478
- Accession Number:187693559
- Copyright Statement:Copyright of Conflict Resolution Quarterly 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.