Effectively measuring variation in institutions over time and across jurisdictions is important for examining how institutional characteristics shape political, social, and economic issues. We present a new dataset of American Indian and Alaska Native (AIAN) constitutions and a new approach for measuring variation in polities using machine learning techniques. Existing data on AIAN institutions have largely been based on costly and time-consuming expert coding and survey approaches, where the end product will become obsolete once institutions change. Our automated content analysis of AIAN constitutional documents allows for more flexible and customizable measurement of the variation, using a larger corpus of data than existing approaches, limited by data collection and coding costs. We consider variation in judicial institutions, previously shown to play a crucial role in AIAN development, and compare our machine coded measures to existing hand coded data for a sample of 97 American Indian constitutions. We show that machine coding replicates expert coded data. Our approach can be easily extended to other topics, including the executive, and shows the potential of automated measures to complement or confirm traditional coding of political institutions.

中文翻译：

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使用自动内容分析衡量美洲印第安人宪法的制度差异

有效衡量制度随时间和跨司法管辖区的变化对于研究制度特征如何影响政治、社会和经济问题非常重要。我们提出了美国印第安人和阿拉斯加原住民 (AIAN) 宪法的新数据集，以及使用机器学习技术衡量政体变化的新方法。AIAN 机构的现有数据主要基于昂贵且耗时的专家编码和调查方法，一旦机构发生变化，最终产品就会过时。我们对 AIAN 宪法文件的自动内容分析允许对变化进行更灵活和可定制的测量，使用比现有方法更大的数据语料库，受数据收集和编码成本的限制。我们考虑司法机构的变化，先前证明在 AIAN 发展中发挥着至关重要的作用，并将我们的机器编码措施与现有的手工编码数据进行比较，以获取 97 份美国印第安宪法的样本。我们展示了机器编码复制了专家编码的数据。我们的方法可以很容易地扩展到其他主题，包括行政部门，并展示了自动化措施在补充或确认传统政治机构编码方面的潜力。