As those of you who attended the 2018 PolMeth meeting witnessed, the
meeting was a great success.  It was the largest and most diverse meeting
in our 35 year history.  I would like to thank Jay Goodliffe, this year's
host, for his tireless efforts to make this possible.  I also thank the
program committee for putting together a fantastic program.

This year, we created two best graduate poster awards, one for methods and
the other for applications.  We had more than 100 graduate student posters
this year, and so it was a very competitive process.  The winners of the
awards are:

Methods: *Erin Rossiter* (Washington University in St. Louis)
“Measuring Agenda-Setting Power in Political Discourse”
Selection committee: Justin Esarey (chair), Ines Levin, Chris Lucas

Applications: *Kelsey Shoub *(University of North Carolina, Chapel Hill)
“How Changing Frame Sets Alters Legislative Outcomes in Congress”
Selection committee: Mark Pickup, Alex Tahk (chair), Michelle Torres

Their citations appear at the bottom of this message.  Congratulations to
Erin and Kelsey.  Thanks to everyone who attended this year's meeting and
we hope to see you next year at MIT.

Best,
Kosuke

Kosuke Imai
Professor of Government and of Statistics, Harvard University
1737 Cambridge Street, Institute for Quantitative Social Science
Cambridge MA 02138.  Phone: 617-384-6778

Award Citation for *Erin Rossiter:*
Erin's project extends topic models for text analysis to measure the extent
to which speakers are able to control the agenda of a political debate or
conversation by shifting the topic. Erin provides careful validation of the
model, along with benchmarks against alternative methods, and demonstrates
the model in applications to deliberative speech and election debates. In
sum, the committee appreciated this poster's combination of methodological
innovation, substantive applicability, clear presentation, and well-thought
out agenda for improvement and extension to future work.

Award Citation for *Kelsey Shoub:*
Shoub’s research project uses supervised and unsupervised machine learning
applied to the Congressional Record to test whether changes in framing
affect policy outcomes in Congress. For bills introduced repeatedly in
Congress, Shoub demonstrates that changes in framing, as measured using
dynamic topic models, increases the probability of passage in each chamber.
The project represents an exemplary use of current text analysis methods to
make valuable substantive contributions.

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