构建美国地方法规语料库，让散落的市政条例可被机器读取和分析
Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States
- 整合9239个市县的法规文本，统一格式并标注元数据
- 覆盖3144个县中的2309个，涵盖多数人口居住区
- 首次实现对地方立法透明度与家长式管控的规模化分析
法律人工智能的发展依赖于大规模权威法律文本。然而，美国法律中最具实际影响的一层——地方条例——仍缺失于现有可机器读取的语料库。这些条例涉及分区、住房、营业执照、公共卫生、噪音、动物管理等日常监管领域，但分散在为人工浏览设计的商业平台中，难以批量获取。本文提出LOCUS——美国地方条例语料库，包含近全部公开的市县级条例文本，共覆盖9239个市县。一个更小但标准化的县级访问层覆盖美国3144个县中的2309个，涵盖多数人口。通过光学字符识别（OCR）处理多种文档格式，使法律成为公共可访问资源。我们提供带元数据的语料库，支持可复现研究及后续扩展。同时训练基于ModernBERT的分类器与评分模型，首次在大规模上分析地方立法的透明度与家长式特征。LOCUS-v1及其衍生模型已发布于Hugging Face：https://huggingface.co/datasets/LocalLaws/LOCUS-v1
原文摘要 · Abstract (English)
Progress in legal AI increasingly depends on access to authoritative legal text at scale. Yet one of the most consequential layers of American law remains largely absent from existing machine-readable corpora: local ordinances. Local codes govern zoning, housing, business licensing, public health, noise, animal control, and many other domains of everyday regulation, but they are fragmented across vendor platforms designed for human browsing rather than bulk research access. We introduce LOCUS - the Local Ordinance Corpus for the United States - a comprehensive corpus and county-harmonized access layer for U.S. municipal and county ordinance codes. The raw corpus, available for release to researchers, represents nearly all publicly available municipal and county ordinance codes. The resulting raw corpus contains codes from 9,239 cities and counties. A smaller county-harmonized LOCUS access layer provides coverage for the largest 2,309 of 3,144 U.S. counties, accounting for a majority of the population. We use OCR to handle the myriad of document formats that have kept the law from being a public resource. We release the corpus with coverage metadata to support reproducibility, downstream legal AI research, and the incremental expansion of machine-readable access to local law. We train a collection of ModernBERT-based classifiers and scorers to facilitate analyzing U.S. local law among several dimensions, such as opacity and paternalism, that have not previously been studied at this scale. LOCUS-v1 and its derivative models are available at: https://huggingface.co/datasets/LocalLaws/LOCUS-v1
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