The majority of studies on international conflict escalation use a variety of measures of hostility including the use of force, reciprocity, and the number of fatalities. The use of different measures, however, leads to different empirical results and creates difficulties when testing existing theories of interstate conflict. Furthermore, hostility measures currently used in the conflict literature are ill suited to the task of identifying consistent predictors of international conflict escalation. This article presents a new dyadic latent measure of interstate hostility, created using a Bayesian item-response theory model and conflict data from the Militarized Interstate Dispute (MID) and Phoenix political event datasets. This model (1) provides a more granular, conceptually precise, and validated measure of hostility, which incorporates the uncertainty inherent in the latent variable; and (2) solves the problem of temporal variation in event data using a varying-intercept structure and human-coded data as a benchmark against which biases in machine-coded data are corrected. In addition, this measurement model allows for the systematic evaluation of how existing measures relate to the construct of hostility. The presented model will therefore enhance the ability of researchers to understand factors affecting conflict dynamics, including escalation and de-escalation processes.

中文翻译：

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衣领下的热度：州际敌意的潜在衡量标准

大多数关于国际冲突升级的研究使用各种敌对措施，包括使用武力、互惠和死亡人数。然而，使用不同的措施会导致不同的实证结果，并在检验现有的国家间冲突理论时造成困难。此外，当前在冲突文献中使用的敌意度量不适合识别国际冲突升级的一致预测因素的任务。本文提出了一种新的州际敌意的二元潜在度量，它使用贝叶斯项目响应理论模型和来自军事化州际争端 (MID) 和凤凰城政治事件数据集的冲突数据创建。该模型 (1) 提供了更细粒度、概念上更精确且经过验证的敌意度量，它结合了潜在变量中固有的不确定性；(2) 使用可变截距结构和人工编码数据作为基准来解决事件数据中的时间变化问题，机器编码数据中的偏差被纠正。此外，该测量模型允许对现有测量与敌意结构的关系进行系统评估。因此，所提出的模型将提高研究人员了解影响冲突动态的因素的能力，包括升级和降级过程。该衡量模型允许系统评估现有衡量标准与敌意结构的关系。因此，所提出的模型将提高研究人员了解影响冲突动态的因素的能力，包括升级和降级过程。该衡量模型允许系统评估现有衡量标准与敌意结构的关系。因此，所提出的模型将提高研究人员了解影响冲突动态的因素的能力，包括升级和降级过程。