Measuring Law Over Time: A Network Analytical Framework with an Application to Statutes and Regulations in the United States and Germany

• URL: http://arxiv.org/abs/2101.11284v2
• Date: Mon, 5 Apr 2021 12:05:07 GMT
• Title: Measuring Law Over Time: A Network Analytical Framework with an Application to Statutes and Regulations in the United States and Germany
• Authors: Corinna Coupette, Janis Beckedorf, Dirk Hartung, Michael Bommarito, and Daniel Martin Katz
• Abstract summary: We present a comprehensive framework for analyzing legal documents as multi-dimensional, dynamic document networks. We demonstrate the utility of this framework by applying it to an original dataset of statutes and regulations from two different countries. We find that at the federal level, the United States legal system is increasingly dominated by regulations, whereas the German legal system remains governed by statutes.
• Score: 2.446672595462589
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How do complex social systems evolve in the modern world? This question lies at the heart of social physics, and network analysis has proven critical in providing answers to it. In recent years, network analysis has also been used to gain a quantitative understanding of law as a complex adaptive system, but most research has focused on legal documents of a single type, and there exists no unified framework for quantitative legal document analysis using network analytical tools. Against this background, we present a comprehensive framework for analyzing legal documents as multi-dimensional, dynamic document networks. We demonstrate the utility of this framework by applying it to an original dataset of statutes and regulations from two different countries, the United States and Germany, spanning more than twenty years (1998-2019). Our framework provides tools for assessing the size and connectivity of the legal system as viewed through the lens of specific document collections as well as for tracking the evolution of individual legal documents over time. Implementing the framework for our dataset, we find that at the federal level, the United States legal system is increasingly dominated by regulations, whereas the German legal system remains governed by statutes. This holds regardless of whether we measure the systems at the macro, the meso, or the micro level.

Related papers

• InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
  InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws. We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries. InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
  arXiv  Detail & Related papers  (2024-06-21T06:19:03Z)
• Examining the Legal Status of Digital Assets as Property: A Comparative Analysis of Jurisdictional Approaches [0.0]
  This paper examines the complex legal landscape surrounding digital assets, analysing how they are defined and regulated as property across various jurisdictions. As digital assets such as cryptocurrencies and non-fungible tokens (NFTs) increasingly integrate with global economies, their intangible nature presents unique challenges to traditional property law concepts. This research presents a comparative analysis, reviewing how different legal systems classify and manage digital assets within property law.
  arXiv  Detail & Related papers  (2024-04-26T04:22:30Z)
• It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
  Attack Graph (AG) represents the best-suited solution to model and analyze multi-step attacks on computer networks. This paper introduces an analysis-driven framework for AG generation. It enables real-time attack path analysis before the completion of the AG generation with a quantifiable statistical significance.
  arXiv  Detail & Related papers  (2023-12-27T10:44:58Z)
• Enhancing Pre-Trained Language Models with Sentence Position Embeddings for Rhetorical Roles Recognition in Legal Opinions [0.16385815610837165]
  The size of legal opinions continues to grow, making it increasingly challenging to develop a model that can accurately predict the rhetorical roles of legal opinions. We propose a novel model architecture for automatically predicting rhetorical roles using pre-trained language models (PLMs) enhanced with knowledge of sentence position information. Based on an annotated corpus from the LegalEval@SemEval2023 competition, we demonstrate that our approach requires fewer parameters, resulting in lower computational costs.
  arXiv  Detail & Related papers  (2023-10-08T20:33:55Z)
• Leveraging Large Language Models for Topic Classification in the Domain of Public Affairs [65.9077733300329]
  Large Language Models (LLMs) have the potential to greatly enhance the analysis of public affairs documents. LLMs can be of great use to process domain-specific documents, such as those in the domain of public affairs.
  arXiv  Detail & Related papers  (2023-06-05T13:35:01Z)
• SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval [75.05173891207214]
  Legal case retrieval plays a core role in the intelligent legal system. Most existing language models have difficulty understanding the long-distance dependencies between different structures. We propose a new Structure-Aware pre-traIned language model for LEgal case Retrieval.
  arXiv  Detail & Related papers  (2023-04-22T10:47:01Z)
• Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks [3.5880535198436156]
  We propose a novel graph-augmented dense statute retriever (G-DSR) model that incorporates the structure of legislation via a graph neural network to improve dense retrieval performance. Experimental results show that our approach outperforms strong retrieval baselines on a real-world expert-annotated SAR dataset.
  arXiv  Detail & Related papers  (2023-01-30T12:59:09Z)
• Entity Graph Extraction from Legal Acts -- a Prototype for a Use Case in Policy Design Analysis [52.77024349608834]
  This paper presents a prototype developed to serve the quantitative study of public policy design. Our system aims to automate the process of gathering legal documents, annotating them with Institutional Grammar, and using hypergraphs to analyse inter-relations between crucial entities.
  arXiv  Detail & Related papers  (2022-09-02T10:57:47Z)
• LexGLUE: A Benchmark Dataset for Legal Language Understanding in English [15.026117429782996]
  We introduce the Legal General Language Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.
  arXiv  Detail & Related papers  (2021-10-03T10:50:51Z)
• \textit{StateCensusLaws.org}: A Web Application for Consuming and Annotating Legal Discourse Learning [89.77347919191774]
  We create a web application to highlight the output of NLP models trained to parse and label discourse segments in law text. We focus on state-level law that uses U.S. Census population numbers to allocate resources and organize government.
  arXiv  Detail & Related papers  (2021-04-20T22:00:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.