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ID: 116463

Ensemble predictions of the 2012 US presidential election / Montgomery, Jacob M; Hollenbach, Florian M; Ward, Michael D

Ward, Michael D	Journal Article
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2012.

Summary/Abstract

For more than two decades, political scientists have created statistical models aimed at generating out-of-sample predictions of presidential elections. In 2004 and 2008, PS: Political Science and Politics published symposia of the various forecasting models prior to Election Day. This exercise serves to validate models based on accuracy by garnering additional support for those that most accurately foretell the ultimate election outcome. Implicitly, these symposia assert that accurate models best capture the essential contexts and determinants of elections. In part, therefore, this exercise aims to develop the "best" model of the underlying data generating process. Scholars comparatively evaluate their models by setting their predictions against electoral results while also giving some attention to the models' inherent plausibility, parsimony, and beauty.

Key Words

Political Scientists ; Presidential Elections

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ID: 156707

Pairwise comparison framework for fast, flexible, and reliable human coding of political texts / Carlson, David; Montgomery, Jacob M

Montgomery, Jacob M	Journal Article
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Summary/Abstract

Scholars are increasingly utilizing online workforces to encode latent political concepts embedded in written or spoken records. In this letter, we build on past efforts by developing and validating a crowdsourced pairwise comparison framework for encoding political texts that combines the human ability to understand natural language with the ability of computers to aggregate data into reliable measures while ameliorating concerns about the biases and unreliability of non-expert human coders. We validate the method with advertisements for U.S. Senate candidates and with State Department reports on human rights. The framework we present is very general, and we provide free software to help applied researchers interact easily with online workforces to extract meaningful measures from texts.

Key Words

Flexible ; Pairwise Comparison Framework ; Fast ; Reliable Human Coding ; Political Texts

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