一个数据集显示了一个世纪的进化在美国法律法典的复杂性
Dawoon Jeong1,2, James Holehouse3, Jisung Yoon4
1Department of Sociology, University of Chicago, Chicago, IL, USA.
Scientific data
|January 6, 2026
概括
本研究量化分析了1926-2023年的"美国法典",揭示了它的结构和语言演变. 这些发现为了解法律体系动态和适应能力提供了经验基础.
科学领域:
- 法律研究 法律研究
- 数据科学数据科学数据科学
- 复杂性研究 复杂性研究
- 机构分析机构分析
背景情况:
- 社会在数字治理,气候政策和公共卫生方面面临复杂的监管要求.
- 了解法律制度的演变和适应能力至关重要,但在经验上未被充分探索.
- 法律的长期结构动态需要进行定量研究.
研究的目的:
- 从1926年到2023年,量化分析"美国法典" (U.S. Code) 的结构和语言演变.
- 为法定制度的跨学科研究提供经验数据集.
- 为了公开提供一个生成人工智能方法来清理历史法律数据.
主要方法:
- 从1926年到2023年美国法典的定量分析.
- 对单词数,词汇,层次组织 (标题,章节,部分,子部分) 和交叉引用进行统计分析.
- 开发和发布用于光学字符识别 (OCR) 数据清理的生成性AI方法.
主要成果:
- 该研究提供了美国法典随时间的结构和语言复杂性的统计数据.
- 创建了一个全面的数据集,详细介绍联邦法定法律的增长和重组.
- 生成型人工智能清洗方法在GitHub上公开提供.
结论:
- 该数据集为法定系统的大规模,长期的跨学科分析提供了经验基础.
- 这项研究有助于理解法律框架的内部逻辑和适应能力.
- 发布的数据和方法有助于在法律研究,数据科学和机构分析中重复使用.
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