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Coding Disaggregated Intrastate Conflict: Machine Processing the Behavior of Substate Actors Over Time and Space

Published online by Cambridge University Press:  04 January 2017

Stephen M. Shellman
Stephen M. Shellman*
Affiliation:
The Institute for the Theory and Practice of International Relations, The College of William and Mary, P.O. Box 8795, Williamsburg, VA 23186
*
e-mail: smshel@wm.edu
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Abstract

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This article describes a new machine-coded event data set specifically designed to study the spatially, temporally, and tactically disaggregated actions of multiple state and nonstate actors in a systematic fashion. The project develops an extensive set of dictionaries for multiple actors and employs a new coding scheme to organize information on such actors and their behavior. The author describes the machine content-analysis methods used to collect the data and the newly developed coding scheme.

Information

Type
Special Issue: The Statistical Analysis of Political Text
Information
Political Analysis , Volume 16 , Issue 4: Special Issue: The Statistical Analysis of Political Text , Autumn 2008, pp. 464 - 477
Copyright
Copyright © The Author 2008. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Author's note: I would like to thank Philip Schrodt for his help and guidance with this project over the last few years. He did everything from answering numerous e-mails to fixing small programming errors in a moment's notice. I could not have completed this project without his time, patience, and support. I would also like to thank Brandon M. Stewart for his valuable research assistance, ideas, and strong work ethic over the years as he worked on this project. Finally, I would like to thank the anonymous reviewers and guest editors for helpful comments on earlier drafts of this essay. Conflict of interest statement: None declared.

References

Bond, Doug, Craig Jenkins, J., Taylor, Charles L., and Schock, Kurt. 1997. Mapping mass political conflict and civil society: The automated development of event data. Journal of Conflict Resolution 41(4): 553–79.CrossRefGoogle Scholar
Buhaug, Halvard, and Gates, Scott. 2002. The geography of civil war. Journal of Peace Research 39(4): 417–33.CrossRefGoogle Scholar
Collier, Paul, and Hoeffler, Anke. 2004. Greed and grievance in civil wars. Oxford Economic Papers 56: 663–595.CrossRefGoogle Scholar
Davenport, Christian. 1995. Multi-dimensional threat perception and state repression: An inquiry into why states apply negative sanctions. American Journal of Political Science 38(3): 683–713.CrossRefGoogle Scholar
Davenport, Christian, and Ball, Patrick. 2002. Views to a kill: exploring the implications of source selection in the case of Guatemalan State Terror, 1977–1995. Journal of Conflict Resolution 46(3): 427–50.CrossRefGoogle Scholar
Fearon, James, and Laitin, David. 2003. Ethnicity, insurgency, and civil war. American Political Science Review 97(1): 75–90.CrossRefGoogle Scholar
Gerner, Deborah J., Schrodt, Philip A., Yilmaz, Ömür, and Abu-Jabr, Rajaa. 2002. Conflict and mediation event observations (CAMEO): A new event data framework for a post cold war world. Presented at the annual meeting of the American Political Science Association, 29 August-1 September 2002. http://web.ku.edu/keds/papers.dir/gerner02.pdf (accessed October 8, 2008).Google Scholar
Goldstein, Joshua S. 1992. A conflict-cooperation scale for WEIS events data. Journal of Conflict Resolution 36(2): 369–85.CrossRefGoogle Scholar
Goldstein, Joshua S., and Pevehouse, J. C. 1997. Reciprocity, bullying, and international cooperation: Time-series analysis of the Bosnia conflict. American Political Science Review 91(3): 515–30.CrossRefGoogle Scholar
Heger, Lindsay, and Salehyan, Idean. 2007. Ruthless rulers: coalition size and the severity of civil conflict. International Studies Quarterly 51(2): 385–403.CrossRefGoogle Scholar
Hegre, Håvard, Ellingsen, Tanja, Gates, Scott, and Gleditsch, Nils Petter. 2001. Toward a democratic civil peace? Democracy, political change, and civil war 1816–1992. American Political Science Review 95(1): 16–33.CrossRefGoogle Scholar
Horne, Cale D., Shellman, Stephen M., and Stewart, Brandon M. 2008. Nickel and DIMEing the adversary: Does it work or PMESII them off? Presented at the annual meeting of the International Studies Association, San Francisco, CA, 26–29 March.Google Scholar
King, Gary, and Lowe, Will. 2003. An automated information extraction tool for international conflict data with performance as good as human coders: A rare events evaluation design. International Organization 57(3): 617–42.CrossRefGoogle Scholar
Lichbach, Mark. 1987. Deterrence or escalation? The puzzle of aggregate studies of repression and dissent. Journal of Conflict Resolution 31(2): 266–97.CrossRefGoogle Scholar
Moore, Will H. 1998. Repression and dissent: Substitution, context, and timing. American Journal of Political Science 42(3): 851–73.CrossRefGoogle Scholar
Moore, Will H. 2000. The repression of dissent: A substitution model of government coercion. Journal of Conflict Resolution 44(1): 107–27.CrossRefGoogle Scholar
Moore, Will H. 2006. A problem with peace science: The dark side of COW. Working paper. http://mailer.fsu.edu/∼whmoore/garnet-whmoore/research/DarkSideofCOW.pdf (accessed October 8, 2008).Google Scholar
Rasler, Karen. 1996. Concessions, repression, and political protest in the Iranian revolution. American Sociological Review 61(1): 132–52.CrossRefGoogle Scholar
Rød, Jan Ketil, and Buhaug, Halvard. 2007. Civil wars: Prospects and problems with the use of local indicators. Paper presented at ScanGIS’2007 Conference. http://www.scangis.org/scangis2007/papers/e7_rod.pdf (accessed October 8, 2008).Google Scholar
Sambanis, Nicolas. 2001. Do ethnic and nonethnic civil wars have the same causes? Journal of Conflict Resolution 45(4): 259–282 CrossRefGoogle Scholar
Schrodt, Philip A. 1998. KEDS: Kansas event data system manual. http://web.ku.edu/keds/software.dir/keds.html (accessed October 8, 2008).Google Scholar
Schrodt, Philip A. 2006. TABARI manual, version 0.5. http://web.ku.edu/keds/tabari.dir/tabari.manual.060228.pdf (accessed October 8, 2008).Google Scholar
Schrodt, Philip A., Davis, Shannon G., and Weddle, Judith L. 1994. Political science: KEDS—A program for the machine coding of event data. Social Science Computer Review 12(4): 561–87.CrossRefGoogle Scholar
Schrodt, Philip A., and Gerner, Deborah J. 1994. Validity assessment of a machine-coded event data set for the Middle East, 1982–1992. American Journal of Political Science 38(3): 825–54.CrossRefGoogle Scholar
Schrodt, Philip A., Simpson, Erin M., and Gerner, Deborah J. 2001. Monitoring conflict using automated coding of newswire sources: A comparison of five geographical regions. Paper presented at the PRIO/Uppsala University/DECRG High-Level Scientific Conference on Identifying Wars: Systematic Conflict Research and Its Utility in Conflict Resolution and Prevention, Uppsala, Sweden 8–9 June 2001.Google Scholar
Schrodt, Philip A. 2008. TABARI manual, version 0.6.3B7. Lawrence, KS: University of Kansas, Lawrence. http://web.ku.edu/keds/tabari.dir/tabari.manual.0.6.3b7.pdf (accessed October 8, 2008).Google Scholar
Schrodt, Philip A. 2007. Inductive event data scaling using item response theory. Paper presented at the annual meeting of the International Studies Association 48th Annual Convention. http://www.allacademic.com/meta/p179617_index.html (accessed October 8, 2008).Google Scholar
Schrodt, Philip A., Yilmaz, Omur, Gerner, Deborah J., and Hermrick, Dennis. 2008. Coding sub-state actors using the CAMEO (conflict and mediation event observations) actor coding framework. Version 1.0B1. Presented at the International Studies Association meeting, San Francisco, 26–29 March. http://web.ku.edu/keds/papers.dir/ISA08.pdf (accessed October 8, 2008).Google Scholar
Shellman, Stephen M. 2006a. Leaders & their motivations: Explaining government-dissident conflict-cooperation processes. Conflict Management & Peace Science 23(1): 73–90.CrossRefGoogle Scholar
Shellman, Stephen M. 2006b. Process matters: Conflict & cooperation in sequential government-dissident interactions. Security Studies 15(4): 563–99.CrossRefGoogle Scholar
Shellman, Stephen M., Hatfield, Clare J., and Mills, Maggie J. Disaggregating actors in intranational conflict. Journal of Peace Research. Forthcoming.Google Scholar
Shellman, Stephen M., Reeves, Andrew M., and Stewart, Brandon M. 2007. Fair & balanced or fit to print: The effects of source bias on event data analysis. http://web.ku.edu/keds/papers.dir/forecasting.html (accessed October 8, 2008).Google Scholar
Siverson, Randolf Martin, and Starr, Harvey. 1991. The diffusion of war: A study of opportunity and willingness. Ann Arbor, MI: University of Michigan.CrossRefGoogle Scholar
Shellman, Stephen M. 2004. Time series intervals and statistical inference: The effects of temporal aggregation on event data analysis. Political Analysis 12: 97–104.CrossRefGoogle Scholar
Thyne, Clayton. 2006. Cheap signals with costly consequences: The effect of interstate relations on civil war, 1948–1992. Journal of Conflict Resolution 50(6): 937–961.CrossRefGoogle Scholar
Tilly, Charles. 1985. Models and realities of popular collective action Working paper no. 10, Center for Studies of Social Change. New York: New School for Social Research.Google Scholar