Most cross-national human rights datasets rely on human coding to produce yearly, country-level indicators of state human rights practices. Hand-coding the documents that contain the information on which these scores are based is tedious and time-consuming, but has been viewed as necessary given the complexity and detail of the information contained in the text. However, advances in automated text analysis have the potential to streamline this process without sacrificing accuracy. In this research note, we take the first step in creating this streamlined process by employing a supervised machine learning automated coding method that extracts specific allegations of physical integrity rights violations from the original text of country reports on human rights. This method produces a dataset including 163,512 unique abuse allegations in 196 countries between 1999 and 2016. This dataset and method will assist researchers of physical integrity rights abuse because it will allow them to produce allegation-level human rights measures that have previously not existed and provide a jumping-off point for future projects aimed at using supervised machine learning to create global human rights metrics.

中文翻译：

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分解镇压：识别人权报告中的身体完整性权利指控

大多数跨国人权数据集依赖于人类编码来产生国家人权实践的年度国家级指标。对包含这些分数所依据的信息的文档进行手工编码既繁琐又耗时，但考虑到文本中包含的信息的复杂性和细节，被认为是必要的。然而，自动化文本分析的进步有可能在不牺牲准确性的情况下简化这一过程。在本研究报告中，我们迈出了创建这一简化流程的第一步，采用了监督机器学习自动编码方法，该方法从国家人权报告的原始文本中提取了关于侵犯身体完整性权利的具体指控。该方法生成的数据集包括 163、