Сравнение на методи

	Militarized Interstate Dispute Analysis	Event Data Analysis of Conflict
Област	International Relations	International Relations
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1996	1994
Създател	Daniel Jones, Stuart Bremer & J. David Singer (Correlates of War project)	Philip Schrodt (KEDS/TABARI); ICEWS team (Boschee et al.)
Тип	Coding and statistical analysis of interstate militarized confrontations	Automated extraction of structured political events from news text
Основополагащ източник	Jones, D. M., Bremer, S. A., & Singer, J. D. (1996). Militarized interstate disputes, 1816–1992: Rationale, coding rules, and empirical patterns. Conflict Management and Peace Science, 15(2), 163–213.	Schrodt, P. A., Davis, S. G., & Weddle, J. L. (1994). Political science: KEDS — A program for the machine coding of event data. Social Science Computer Review, 12(4), 561–588. See also Gerner, Schrodt et al. (1994), Machine coding of event data using regional and international sources, International Studies Quarterly, 38(1), 91–119.
Други названия	MID Analysis, Militarized Dispute Coding, Correlates of War Dispute Analysis, Dyadic Conflict Onset Analysis	Political Event Data, Machine-Coded Conflict Event Data, Conflict Event Extraction, Who-Did-What-to-Whom Event Coding
Свързани	3	4
Резюме	Militarized interstate dispute (MID) analysis is the coding and quantitative study of confrontations in which one state threatens, displays, or uses military force against another. Built on the Correlates of War project's MID dataset and the coding rules codified by Jones, Bremer, and Singer (1996), it provides the standard observational measure of interstate conflict short of and including war, structured as dyad-years so that the onset, escalation, and outcomes of disputes can be modeled statistically across two centuries of the international system.	Event data analysis is the automated extraction of structured records of political interactions — who did what to whom, when, and where — from large volumes of news text, for the quantitative study of conflict and cooperation. Pioneered for machine coding by Philip Schrodt with the KEDS and TABARI systems and scaled in projects such as ICEWS and GDELT, it turns unstructured reporting into dated actor-action-target triples coded to an ontology like CAMEO, which can then be aggregated into time series of interstate or intrastate hostility.