Article I, Section 4 of the Constitution, the Voting Rights Act, and Restoration of the Congressional Portion of the Election Ballot: The Final Frontier of Felon Disenfranchisement Jurisprudence?

Article published in the Journal of Law and Social Change

Quantitative Legal Prediction--or--How I Learned to Stop Worrying and Start Preparing for the Data-Driven Future of the Legal Services Industry

Welcome to law\u27s information revolution-revolution already in progress

On the Stability of Community Detection Algorithms on Longitudinal Citation Data

There are fundamental differences between citation networks and other classes of graphs. In particular, given that citation networks are directed and acyclic, methods developed primarily for use with undirected social network data may face obstacles. This is particularly true for the dynamic development of community structure in citation networks. Namely, it is neither clear when it is appropriate to employ existing community detection approaches nor is it clear how to choose among existing approaches. Using simulated data, we attempt to clarify the conditions under which one should use existing methods and which of these algorithms is appropriate in a given context. We hope this paper will serve as both a useful guidepost and an encouragement to those interested in the development of more targeted approaches for use with longitudinal citation data.Comment: 17 pages, 7 figures, presenting at Applications of Social Network Analysis 2009, ETH Zurich Edit, August 17, 2009: updated abstract, figures, text clarification

A General Approach for Predicting the Behavior of the Supreme Court of the United States

Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time evolving random forest classifier which leverages some unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications.Comment: version 2.02; 18 pages, 5 figures. This paper is related to but distinct from arXiv:1407.6333, and the results herein supersede arXiv:1407.6333. Source code available at https://github.com/mjbommar/scotus-predict-v

Hustle and Flow: A Social Network Analysis of the American Federal Judiciary

Scholars have long asserted that social structure is an important feature of a variety of societal institutions. As part of a larger effort to develop a fully integrated model of judicial decision making, we argue that social structure—operationalized as the professional and social connections between judicial actors—partially directs outcomes in the hierarchical federal judiciary. Since different social structures impose dissimilar consequences upon outputs, the precursor to evaluating the doctrinal consequences that a given social structure imposes is a descriptive effort to characterize its nature. Given the difficulty associated with obtaining appropriate data for federal judges, it is necessary to rely upon a proxy measure to paint a picture of the social landscape. In the aggregate, we believe the flow of law clerks reflects a proxy for social and professional linkages between jurists. Having collected available information for all federal judicial law clerks employed by an Article III judge during the “natural” Rehnquist Court (1995-2004), we use these nearly 20,000 clerk events to craft a series of network based visualizations. Using network analysis, our visualizations and subsequent analytics provide insight into the path of peer effects in the federal judiciary. For example, we find the distribution of “degrees” is consistent with the power law distribution implying the social structure is dictated by a small number of socially prominent actors. Drawing from the complex systems literature, our findings suggest federal judicial actors self-organize at positions of criticality, possibly through Yule’s Law. In sum, if social structure “matters” then our results have significant implications for doctrinal phase transition and the “evolution” of the law

Hustle and Flow: A Social Network Analysis of the American Federal Judiciary

Scholars have long asserted that social structure is an important feature of a variety of societal institutions. As part of a larger effort to develop a fully integrated model of judicial decision making, we argue that social structure—operationalized as the professional and social connections between judicial actors—partially directs outcomes in the hierarchical federal judiciary. Since different social structures impose dissimilar consequences upon outputs, the precursor to evaluating the doctrinal consequences that a given social structure imposes is a descriptive effort to characterize its nature. Given the difficulty associated with obtaining appropriate data for federal judges, it is necessary to rely upon a proxy measure to paint a picture of the social landscape. In the aggregate, we believe the flow of law clerks reflects a proxy for social and professional linkages between jurists. Having collected available information for all federal judicial law clerks employed by an Article III judge during the “natural” Rehnquist Court (1995-2004), we use these nearly 20,000 clerk events to craft a series of network based visualizations. Using network analysis, our visualizations and subsequent analytics provide insight into the path of peer effects in the federal judiciary. For example, we find the distribution of “degrees” is consistent with the power law distribution implying the social structure is dictated by a small number of socially prominent actors. Drawing from the complex systems literature, our findings suggest federal judicial actors self-organize at positions of criticality, possibly through Yule’s Law. In sum, if social structure “matters” then our results have significant implications for doctrinal phase transition and the “evolution” of the law