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Justin Grimmer (Stanford University)
Citation
The winner of the 2014 Society for Political Methodology Emerging Scholar Award is Justin Grimmer from Stanford University. The committee received many excellent nominations and we thank our many colleagues who suggested such an excellent pool of deserving candidates. After many detailed discussions and having settled on a criteria of notable contributions that might best be captured by a summary measure that weights the quality, quantity, and impact of the work to date unconditional on the date of the Ph.D. the committee was unanimous in its choice of Justin Grimmer.
In our opinion, Grimmer is at the leading edge of a push by younger scholars into "audacious" data collection in American politics, bringing new tools from computer science and machine learning to harvest vast quantities of data, almost all of it beginning life as text (in electronic format) and to categorize/classify the content of that data, making it a vital component of new research programs, or breathing new life into established research programs. His 2013 Political Analysis article --Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Documents for example, (which was awarded the 2013 Editors Choice award) is an excellent introduction for political scientists interested in learning about methods useful for analyzing textual data. His 2010 Warren Miller winning paper published in Political Analysis -- A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases delves more specifically into one approach and it nicely demonstrates how a hierarchical model applied to textual analysis can help improve our ability to characterize and compare important and politically relevant patterns in political speech. His 2011 Political Analysis article An Introduction to Bayesian Inference Via Variational Approximations is also a nice introduction to Machine Learning tools that have been used in other disciplines and which are only just beginning to appear in political science.
Importantly, the methodological tools that Justin has been a leading advocate for has had an important impact on our substantive understanding of the representational relationship between elected officials and their constituents. This is evident not only because of Justins own work which is a rigorous, data-based recasting of Fennos Homestyle and Mayhews categorizations of congressional behavior that resulted in being awarded the 2014 Richard J. Fenno, Jr. Prize for the best book in legislative studies published in 2013 but also because of his work advocating and introducing methods that will allow other scholars to both build upon this work and also it to other contexts.