Justin Grimmer

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Finansman sağlayan kuruluşun getirdiği zorunluluğa dayalı olarak

Katkıda bulunan yazarlar

Brandon M. StewartPrinceton Universityprinceton.edu üzerinde doğrulanmış e-posta adresine sahip
Sean J WestwoodAssociate Professor of Government, Dartmouth Collegedartmouth.edu üzerinde doğrulanmış e-posta adresine sahip
Margaret E. RobertsPolitical Science, University of California San Diegoucsd.edu üzerinde doğrulanmış e-posta adresine sahip
Solomon MessingNew York Universitystanford.edu üzerinde doğrulanmış e-posta adresine sahip
Clayton NallAssociate Professor of Political Science, UC Santa Barbaraucsb.edu üzerinde doğrulanmış e-posta adresine sahip
Eleanor Neff PowellAssociate Professor of Political Science, University of Wisconsin-Madisonwisc.edu üzerinde doğrulanmış e-posta adresine sahip
Gary KingAlbert J Weatherhead III University Professor, Harvard Universityharvard.edu üzerinde doğrulanmış e-posta adresine sahip
Eitan HershProfessor of Political Sciencetufts.edu üzerinde doğrulanmış e-posta adresine sahip
Daniel CarpenterAllie S. Freed Professor of Government, Harvard Universitygov.harvard.edu üzerinde doğrulanmış e-posta adresine sahip
Jonathan MummoloAssociate Professor of Politics and Public Affairs, Princeton Universityprinceton.edu üzerinde doğrulanmış e-posta adresine sahip

Takip et

Justin Grimmer

Morris M. Doyle Centennial Professor. Senior Fellow, Hoover Institution. Stanford University

stanford.edu üzerinde doğrulanmış e-posta adresine sahip - Ana Sayfa

Election Administration Political Communication Text as Data Machine Learning Representation


Başlık Alıntılara göre sırala Yıla göre sırala Başlığa göre sırala	Alıntı yapanlar Alıntı yapanlar	Yıl
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts J Grimmer, BM Stewart	4216	2011
A Bayesian hierarchical topic model for political texts: Measuring expressed agendas in Senate press releases J Grimmer Political Analysis 18 (1), 1-35, 2010	893	2010
Text as data: A new framework for machine learning and the social sciences J Grimmer, ME Roberts, BM Stewart Princeton University Press, 2022	451	2022
Representational style in Congress: What legislators say and why it matters J Grimmer Cambridge University Press, 2013	402	2013
How words and money cultivate a personal vote: The effect of legislator credit claiming on constituent credit allocation J Grimmer, S Messing, SJ Westwood American Political Science Review 106 (4), 703-719, 2012	354	2012
We are all social scientists now: How big data, machine learning, and causal inference work together J Grimmer PS: Political Science & Politics 48 (1), 80-83, 2015	331	2015
Machine learning for social science: An agnostic approach J Grimmer, ME Roberts, BM Stewart Annual Review of Political Science 24 (1), 395-419, 2021	328	2021
General purpose computer-assisted clustering and conceptualization J Grimmer, G King Proceedings of the National Academy of Sciences 108 (7), 2643, 2011	287	2011
Appropriators not position takers: The distorting effects of electoral incentives on congressional representation J Grimmer American Journal of Political Science 57 (3), 624-642, 2013	278	2013
Causal inference in natural language processing: Estimation, prediction, interpretation and beyond A Feder, KA Keith, E Manzoor, R Pryzant, D Sridhar, Z Wood-Doughty, ... Transactions of the Association for Computational Linguistics 10, 1138-1158, 2022	275	2022
Money in exile: Campaign contributions and committee access EN Powell, J Grimmer The Journal of Politics 78 (4), 974-988, 2016	244	2016
How to make causal inferences using texts N Egami, CJ Fong, J Grimmer, ME Roberts, BM Stewart Science Advances 8 (42), eabg2652, 2022	232	2022
Estimating heterogeneous treatment effects and the effects of heterogeneous treatments with ensemble methods J Grimmer, S Messing, SJ Westwood Political Analysis 25 (4), 413-434, 2017	223	2017
The impression of influence: legislator communication, representation, and democratic accountability J Grimmer, SJ Westwood, S Messing Princeton University Press, 2014	182*	2014
Are Close Elections Random? J Grimmer, E Hersh, B Feinstein, D Carpenter Unpublished manuscript, 2011	172	2011
Obstacles to estimating voter ID laws’ effect on turnout J Grimmer, E Hersh, M Meredith, J Mummolo, C Nall	170*	2017
An Introduction to Bayesian Inference via Variational Approximations J Grimmer Political Analysis 19 (1), 32, 2011	156	2011
Current research overstates American support for political violence SJ Westwood, J Grimmer, M Tyler, C Nall Proceedings of the National Academy of Sciences 119 (12), e2116870119, 2022	154	2022
No evidence for systematic voter fraud: A guide to statistical claims about the 2020 election AC Eggers, H Garro, J Grimmer Proceedings of the National Academy of Sciences 118 (45), e2103619118, 2021	124	2021
Mirrors for princes and sultans: advice on the art of governance in the medieval Christian and Islamic worlds L Blaydes, J Grimmer, A McQueen The Journal of Politics 80 (4), 1150-1167, 2018	110	2018

Sistem, işlemi şu anda gerçekleştiremiyor. Daha sonra yeniden deneyin.

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