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Temporal analysis of political instability through descriptive subgroup discovery
[journal article]
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... view more
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.... view less
Keywords
methodology; political stability; research approach; conflict theory; theory; conflict management; cause; political violence; failed state
Classification
Research Design
Peace and Conflict Research, International Conflicts, Security Policy
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
https://doi.org/10.1080/07388940701860359
ISSN
1549-9219
Status
Published Version; peer reviewed
Licence
Deposit Licence - No Redistribution, No Modifications
With the permission of the rights owner, this publication is under open access due to a (DFG-/German Research Foundation-funded) national or Alliance license.