Structure and Content in the United States Code

2020 ◽  
Author(s):  
Keith Carlson ◽  
Faraz Dadgostari ◽  
Michael A. Livermore ◽  
Daniel Rockmore
Keyword(s):  
United States ◽  
The United States ◽  
United States Code

Copyright Revision in the United States of America

1977 ◽  
Vol 5 (1) ◽  
pp. 121-130
Author(s):  
Julius J. Marke
Keyword(s):  
United States ◽  
United States Of America ◽  
The United States ◽  
Public Law ◽  
United States Code

Title 17 of the United States Code (Copyright Act of 1909) was amended in its entirety by Public Law 94–553 (90 Stat. 2541), on October 19, 1976. The new Statute specifies that, with particular exceptions, its provisions are to enter into force on January 1, 1978. Until then, the Copyright Act of 1909, as amended, remains in force. As a result, any cause of action in copyright arising before December 31, 1977 will be governed by the 1909 law. There is a three-year statute of limitations in which such an action may be brought. Hence, the 1909 law can be applicable in these cases until 1981.

scholarly journals A Multinetwork and Machine Learning Examination of Structure and Content in the United States Code

2021 ◽  
Vol 8 ◽  
Author(s):  
Keith Carlson ◽  
Faraz Dadgostari ◽  
Michael A. Livermore ◽  
Daniel N. Rockmore
Keyword(s):  
United States ◽  
Machine Learning ◽  
Topic Model ◽  
Citation Network ◽  
The United States ◽  
Hierarchical Organization ◽  
Machine Learning Techniques ◽  
Prediction Task ◽  
Network Layers ◽  
United States Code

This paper introduces a novel linked structure-content representation of federal statutory law in the United States and analyzes and quantifies its structure using tools and concepts drawn from network analysis and complexity studies. The organizational component of our representation is based on the explicit hierarchical organization within the United States Code (USC) as well an embedded cross-reference citation network. We couple this structure with a layer of content-based similarity derived from the application of a “topic model” to the USC. The resulting representation is the first that explicitly models the USC as a “multinetwork” or “multilayered network” incorporating hierarchical structure, cross-references, and content. We report several novel descriptive statistics of this multinetwork. These include the results of this first application of the machine learning technique of topic modeling to the USC as well as multiple measures articulating the relationships between the organizational and content network layers. We find a high degree of assortativity of “titles” (the highest level hierarchy within the USC) with related topics. We also present a link prediction task and show that machine learning techniques are able to recover information about structure from content. Success in this prediction task has a natural interpretation as indicating a form of mutual information. We connect the relational findings between organization and content to a measure of “ease of search” in this large hyperlinked document that has implications for the ways in which the structure of the USC supports (or doesn’t support) broad useful access to the law. The measures developed in this paper have the potential to enable comparative work in the study of statutory networks that ranges across time and geography.

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