Quantitative Human Rights

Amanda M. Murdie, K. A. Watson
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Abstract

Quantitative human rights scholarship is increasing. New data sets and methods have helped researchers examine a broad array of research questions concerning the many human rights laid out in the United Nations’ 1948 Universal Declaration of Human Rights and related documents. These innovations have enabled quantitative human rights scholarship to better connect to existing qualitative and theoretical literatures and have improved advocacy efforts. Quantitative scholars have primarily operationalized the concept of human rights through the use of four kinds of data: events data (such as counts of abuses or attacks), standards-based data (such as coded scores), survey data, and socioeconomic statistics (such as maternal mortality or malnutrition rates). Each type of data poses particular challenges and weaknesses for analyses, including the biased undercounts of events data and the potential for human error or biases in survey or standards-based data. The human rights field has also seen a systematic overrepresentation of analyses of physical integrity rights, which have fewer component parts to measure. Furthermore, qualitative scholars have pointed out that it is difficult for quantitative data to capture the process of human rights improvement over time. The creation of new technologies and methodologies has allowed quantitative researchers to lessen the impact of these data weaknesses: Latent variables allow scholars to create aggregate measures from a variety of classes of quantitative data, as well as understandings from qualitative scholars, leading to the creation of new measures for rights other than physical integrity rights. New machine learning techniques and algorithms are giving scholars access to greater amounts of data than ever before, improving event counts. Expert surveys are pulling new voices into the data-generating process and incorporating practitioners into data processes that are too often restricted to academics. Experimental studies are furthering the field’s understanding of the processes underlying advocacy. Drawing on the lessons of past work, future scholars can use quantitative methods to improve the field’s theoretical and practical understandings of human rights.
定量人权
定量的人权研究正在增加。新的数据集和方法帮助研究人员检查了一系列广泛的研究问题,这些问题涉及联合国1948年《世界人权宣言》和相关文件中列出的许多人权。这些创新使定量人权研究能够更好地与现有的定性和理论文献联系起来,并改进了宣传工作。定量学者主要通过使用四种数据来实现人权概念:事件数据(如虐待或攻击的次数)、基于标准的数据(如编码分数)、调查数据和社会经济统计(如孕产妇死亡率或营养不良率)。每种类型的数据都对分析提出了特定的挑战和弱点,包括对事件数据的有偏见的低估,以及在调查或基于标准的数据中存在人为错误或偏见的可能性。人权领域也出现了对人身完整权利的系统性过度分析,可衡量的组成部分较少。此外,定性学者指出,定量数据很难反映一段时间以来人权改善的过程。新技术和新方法的创造使定量研究人员能够减轻这些数据弱点的影响:潜在变量使学者能够从各种类型的定量数据中创建总体衡量标准,以及定性学者的理解,从而创建除人身完整权之外的其他权利的新衡量标准。新的机器学习技术和算法使学者们能够获得比以往更多的数据,从而提高了事件数量。专家调查正在将新的声音引入数据生成过程,并将从业者纳入通常仅限于学术界的数据处理过程。实验研究进一步加深了该领域对倡导背后的过程的理解。借鉴过去工作的经验教训,未来的学者可以使用定量方法来提高该领域对人权的理论和实践理解。
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