Professor Dr. Christina L. Boyd of the State University of New York (SUNY) – Department of Political Science, Professor David A. Hoffman of the Temple University School of Law and the Cultural Cognition Project at Yale Law School, and colleagues, have posted Building a Taxonomy of Litigation: Clusters of Causes of Action in Federal Complaints.
This article has been published in: Journal of Empirical Legal Studies, 10(2), 253-287 (2013): http://dx.doi.org/10.1111/jels.12010
Here is the abstract:
This project empirically explores civil litigation from its inception by examining the content of civil complaints. We utilize spectral cluster analysis on a newly compiled federal district court dataset of causes of action in complaints to illustrate the relationship of legal claims to one another, the broader composition of lawsuits in trial courts, and the breadth of pleading in individual complaints. Our results shed light not only on the networks of legal theories in civil litigation but also on how lawsuits are classified and the strategies that plaintiffs and their attorneys employ when commencing litigation. This approach permits us to lay the foundations for a more precise and useful taxonomy of federal litigation than has been previously available, one that, after the Supreme Court’s recent decisions in Bell Atlantic v. Twombly (2007) and Ashcroft v. Iqbal (2009), has also arguably never been more relevant than it is today.
This study is notable for several reasons, including that Computational Legal Studies founders Professor Dr. Daniel Martin Katz and Michael Bommarito commented on the statistical methodology used in the study, and that the study uses government data made public through RECAP, the open government data project developed by Harlan Yu, Stephen Schultze, and Timothy B. Lee, all of Princeton’s Center for Information Technology Policy.
Further, this study exemplifies the scholarly use of open government data predicted by David Robinson, Harlan Yu, and Ed Felten, in their influential article, Government Data and the Invisible Hand.