Structure and Content in the United States Code
Structure and Content in the United States Code
复制标题
美国法典的结构和内容
DOI：
10.2139/ssrn.3690508
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
D. Rockmore
中科院分区：
文献类型：
--
作者：
Keith Carlson;Faraz Dadgostari;Michael A. Livermore;D. Rockmore
本文探讨了法定结构--通过层级组织和交叉引用网络实现--与语义内容之间的关系。我们报告了几个关于美国法典(USC)的新的描述性统计数据，包括首次将主题建模的机器学习技术应用于该语料库的结果。我们发现，正如南加州大学的主题组织所表达的那样，主题模型在发现相关法律类别方面表现得相当好。我们估计了形式结构与主题内容之间的关系，发现“标题”(南加州大学内部的最高层次)的分类性与相关主题的分类性高度相似。我们还检查了法定结构和内容之间的互信息程度，发现替代的机器学习技术能够从内容中恢复关于结构的信息，这表明了一定程度的互信息。我们的分析可以用来开发更好的法律复杂性衡量标准，并有可能改进旨在了解法律复杂性对社会结果(如合规成本或经济增长)的重要性的研究。我们的工作还可能应用于法律搜索的研究和开发便利公众获取法律的工具。
This paper explores the relationship between statutory structure—as realized through hierarchical organization and a cross-reference citation network—and semantic content. We report several novel descriptive statistics concerning the United States Code (USC), including the results of the first application to this corpus of the machine learning technique of topic modeling. We find that the topic model performs quite well in discovering relevant legal categories, as expressed in the subject-matter organization of the USC. We estimate relationships between formal structure and topic content and find that the assortativity of “titles” (the highest level hierarchy within the USC) and the assortativity of related topics are highly similar. We also examine the degree of mutual information between statutory structure and content and find that alternative machine learning techniques are able to recover information about structure from content, indicating some level of mutual information. Our analysis can be used to develop superior measures of legal complexity with the potential to improve studies that seek to understand the importance of legal complexity for social outcomes (such as compliance costs or economic growth). Our work also has potential applications for the study of law search and in developing tools to facilitate public access to the law.