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Large language models (LLMs) offer substantial promise for text classification in political science, yet their effectiveness often depends on high-quality prompts and exemplars.
“How to Fine-Tune BERT for Text Classification?”, 2020
Chi Sun, Xipeng Qiu, Yige Xu and Xuanjing Huang · 1905
Earlier work this paper cites.
“Distance between sets”
Michael Levandowsky and David Winter · 1971
Earlier work this paper cites.
“A Method of Scaling with Applications to the 1968 and 1972 Presidential Elections”
John Aldrich and Richard McKelvey · 1977
Earlier work this paper cites.
“Introduction to factor analysis: What it is and how to do it”
JO Kim · 1978
Earlier work this paper cites.
“A spatial model for legislative roll call analysis”
Keith Poole and Howard Rosenthal · 1985
Earlier work this paper cites.
“Principal component analysis”
Svante Wold, Kim Esbensen and Paul Geladi · 1987
Earlier work this paper cites.
“Independent component analysis: algorithms and applications”
Aapo Hyvärinen and Erkki Oja · 2000
Earlier work this paper cites.
“Content analysis in mass communication: Assessment and reporting of intercoder reliability”
Matthew Lombard, Jennifer Snyder-Duch and Cheryl Bracken · 2002
Earlier work this paper cites.
“The content analysis guidebook sage publications, Inc”
K Neuendorf · 2002
Earlier work this paper cites.
“Support vector machines for text categorization”
A. Basu, C. Walters and M. Shepherd · 2003
Earlier work this paper cites.
“Language Models are Few-Shot Learners”, 2020
Tom. Brown et al · 2005
Earlier work this paper cites.
“Modern multidimensional scaling: Theory and applications”
Ingwer Borg and Patrick Groenen · 2007
Earlier work this paper cites.
“MMR: An algorithm for clustering categorical data using Rough Set Theory” 25th International Conference on Conceptual Modeling (ER 2006)
Darshit Parmar, Teresa Wu and Jennifer Blackhurst · 2007
Earlier work this paper cites.
“Inter-coder agreement for computational linguistics”
Ron Artstein and Massimo Poesio · 2008
Earlier work this paper cites.
“A scaling model for estimating time-series party positions from texts”
Jonathan Slapin and Sven-Oliver Proksch · 2008
Earlier work this paper cites.
“A Method of Automated Nonparametric Content Analysis for Social Science”
Daniel. Hopkins and Gary King · 2010
Earlier work this paper cites.
“Prototype selection for interpretable classification”
Jacob Bien and Robert Tibshirani · 2011
Earlier work this paper cites.
“Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts”
Justin Grimmer and Brandon. Stewart · 2013
Earlier work this paper cites.
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“Twitter style: An analysis of how house candidates used Twitter in their 2012 campaigns”
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“Understanding the Political Representativeness of Twitter Users”
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“Quantifying social media’s political space: Estimating ideology from publicly revealed preferences on Facebook”
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“Social media and fake news in the 2016 election”
“A survey on in-context learning”
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“Large language models are zero-shot reasoners”
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“Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?”
Sewon Min et al · 2022
Later among the works it cites.
“Emergent abilities of large language models”
Jason Wei et al · 2022
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“Large language models are human-level prompt engineers”
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Later among the works it cites.
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