ABSTRACT

Scholars have been interested in the politicization of humans rights within the United Nations for some time. However, previous research typically looks at simple associations between voting coalitions and observable variables, such as geographic location or membership in international organizations. Our study is the first attempt at estimating the latent coalition structure based on the voting data. We propose a Bayesian Dynamic Dirichlet Process Mixture (DDPM) model to identify voting coalitions based on roll call vote data across multiple time periods. We also propose post-processing methods for analyzing the outputs of the DDPM model. We apply these methods to the United Nations General Assembly (UNGA) human rights roll call vote data from 1992 to 2017. We identify human rights voting coalitions in the UNGA after the Cold War, and polarizing resolutions that divide countries into different coalitions.

摘要

一段时间以来，学者们一直对联合国内部的人权政治化感兴趣。然而，以前的研究通常着眼于投票联盟和可观察变量之间的简单关联，例如地理位置或国际组织的成员资格。我们的研究是基于投票数据估计潜在联盟结构的第一次尝试。我们提出了贝叶斯动态狄利克雷过程混合 (DDPM) 模型，以根据多个时间段的点名投票数据来识别投票联盟。我们还提出了用于分析 DDPM 模型输出的后处理方法。我们将这些方法应用于联合国大会 (UNGA) 1992 年至 2017 年的人权唱名投票数据。我们确定冷战后联合国大会的人权投票联盟，