Tae Yano and Professor Dr. Noah A. Smith, both of Carnegie Mellon University Language Technologies Institute, and Professor Dr. John D. Wilkerson of the University of Washington Deaprtment of Political Science, presented a paper entitled Textual Predictors of Bill Survival in Congressional Committees, at New Directions in Analyzing Text as Data 2012, a conference held 5-6 October 2012 at the Harvard University Institute for Quantitative Social Science.
Here is the abstract:
A U.S. Congressional bill is a textual artifact that must pass through a series of hurdles to become a law. In this paper, we focus on one of the most precarious and least understood stages in a bill’s life: its consideration, behind closed doors, by a Congressional committee. We construct predictive models of whether a bill will survive committee, starting with a strong, novel baseline that uses features of the bill’s sponsor and the committee it is referred to. We augment the model with information from the contents of bills, comparing different hypotheses about how a committee decides a bill’s fate. These models give significant reductions in prediction error and highlight the importance of bill substance in explanations of policy-making and agenda-setting.
A notable technology related to this research is the new probability-of-bill-passage feature on Dr. Joshua Tauberer’s GovTrack service.
Click here for Dr. Tauberer’s comment on the Yano et al. paper.
An interesting discussion among academics and developers arose on Twitter in response to a tweet about this paper.
Tags: GovTrack, John D. Wilkerson, Joshua Tauberer, Legislative information systems, New Directions in Analyzing Text as Data, New Directions in Analyzing Text as Data 2012, Noah A. Smith, Prediction in legal informatics, Prediction in legal information systems, Quantitative legal prediction, Statistical analysis of legislative data, Statistical methods in legal informatics, Statistical prediction of bill passage success, Statistical prediction of committee approval of a bill, Tae Yano, Text as Data Conference, Text as Data Conference 2012
