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Temporal analysis of political instability through descriptive subgroup discovery

[journal article]

Lambach, Daniel; Gamberger, Dragan

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Please use the following Persistent Identifier (PID) to cite this document:http://nbn-resolving.de/urn:nbn:de:0168-ssoar-368876

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Abstract This paper analyzes the Political Instability Task Force (PITF) data set using a new methodology based on machine learning tools for subgroup discovery. While the PITF used static data, this study employs both static and dynamic descriptors covering the 5-year period before onset. The methodology provides several descriptive models of countries especially prone to political instability. For the most part, these models corroborate the PITF’s findings and support earlier theoretical works. The paper also shows the value of subgroup discovery as a tool for developing a unified concept of political instability as well as for similar research designs.
Keywords failed state; conflict theory; conflict management; political stability; political violence; cause; research approach; methodology; theory
Classification Peace and Conflict Research, International Conflicts, Security Policy; Research Design
Method basic research; development of methods
Free Keywords Fragile Staaten/ Gescheiterte Staaten; Instabilität
Document language English
Publication Year 2008
Page/Pages p. 19-32
Journal Conflict Management and Peace Science, 25 (2008) 1
DOI http://dx.doi.org/10.1080/07388940701860359
ISSN 1549-9219
Status Published Version; peer reviewed
Licence Deposit Licence - No Redistribution, No Modifications
This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.