The New Rivalry Dataset: Procedures and Patterns

The New Rivalry Dataset: Procedures and Patterns
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新的竞争数据集:程序和模式

DOI:
10.1177/0022343306063935
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发表时间:
2006
影响因子:
3.6
通讯作者:
P. Diehl
P. Diehl
中科院分区:
法学1区
文献类型:
--
作者:
James P. Klein;Gary Goertz;P. Diehl

文献摘要

被引文献

相似文献

20世纪90年代,出现了一个新的研究议程,重点是持久的对抗,同一对国家之间的长期竞争。最初的Diehl & Goertz国际竞争数据集可能是研究这些竞争最广泛使用的数据集。在这里,该数据集扩展到2001年,并使用时间密度方法之外的其他标准来定义竞争群体。在文章的前半部分,描述了最初的竞争收集所基于的概念和操作基础。本文探讨了竞争概念的每个维度和相关的操作标准。竞争概念的“相关冲突”层面在竞争层面的讨论中更加明确。文章然后介绍和讨论了所有的主要变化,维斯较早的竞争收集。在文章的后半部分,实证分析强调了竞争的概念维度。特别注意的是专门的竞争对称性的问题,与竞争对手的权力能力的调查。在对相关冲突维度的分析中,本文考察了战争的发生和竞争的顺序(其中大多数发生在或接近竞争的开始)以及争端之间的结果和等待时间。文章最后将该数据集与另一个著名的竞争对手的数据集进行了比较。
The 1990s saw the emergence of a new research agenda focused on enduring rivalries, longstanding competitions between the same pair of states. The original Diehl & Goertz dataset on international rivalries has been perhaps the most widely used collection to study those rivalries. Here, that dataset is extended through 2001, and additional criteria beyond the time-density approach are used to define a population of rivalries. In the first half of the article, the conceptual and operational bases on which the original rivalry collection was based are described. The article explores each of the dimensions of the rivalry concept and the associated operational criteria. The ‘linked conflict’ dimension of the rivalry concept is made more explicit in the discussion of rivalry dimensions. The article then presents and discusses all the major changes made vis-‡-vis the earlier rivalry collection. In the second half of the article, empirical analyses highlight the conceptual dimensions of rivalry. Particular attention is devoted to the issue of rivalry symmetry, with an investigation of rival power capabilities. In an analysis on the linked conflict dimension, the article examines war occurrence and sequence in rivalry (most of which occurs at or near the outset of the rivalry) as well as the outcome and waiting times between disputes. The article concludes with a comparison of this dataset to another prominent rivalry collection.