Journal article | Zeitschriftenartikel
Temporal analysis of political instability through descriptive subgroup discovery
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.
- ISSN
-
1549-9219
- Umfang
-
Seite(n): 19-32
- Sprache
-
Englisch
- Anmerkungen
-
Status: Veröffentlichungsversion; begutachtet (peer reviewed)
- Erschienen in
-
Conflict Management and Peace Science, 25(1)
- Thema
-
Politikwissenschaft
Sozialwissenschaften, Soziologie
Forschungsarten der Sozialforschung
Friedens- und Konfliktforschung, Sicherheitspolitik
Theorie
Methodologie
Forschungsansatz
Konfliktregelung
Failed State
Ursache
politische Gewalt
politische Stabilität
Konflikttheorie
Grundlagenforschung
Methodenentwicklung
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Lambach, Daniel
Gamberger, Dragan
- Ereignis
-
Veröffentlichung
- (wo)
-
Vereinigte Staaten von Amerika
- (wann)
-
2008
- DOI
- URN
-
urn:nbn:de:0168-ssoar-368876
- Rechteinformation
-
GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln
- Letzte Aktualisierung
-
21.06.2024, 16:27 MESZ
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GESIS - Leibniz-Institut für Sozialwissenschaften. Bibliothek Köln. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Zeitschriftenartikel
Beteiligte
- Lambach, Daniel
- Gamberger, Dragan
Entstanden
- 2008
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