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Disentangling Active and Passive Cosponsorship in the U.S. Congress

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arxiv 2205.09674 v1 pith:YWIFRFGM submitted 2022-05-19 cs.LG cs.CLcs.CYphysics.data-anstat.ML

classification cs.LGcs.CLcs.CYphysics.data-anstat.ML
keywords cosponsorshipactivepassiverepresentationsbackingbillcongresspredict
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In the U.S. Congress, legislators can use active and passive cosponsorship to support bills. We show that these two types of cosponsorship are driven by two different motivations: the backing of political colleagues and the backing of the bill's content. To this end, we develop an Encoder+RGCN based model that learns legislator representations from bill texts and speech transcripts. These representations predict active and passive cosponsorship with an F1-score of 0.88. Applying our representations to predict voting decisions, we show that they are interpretable and generalize to unseen tasks.

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  1. The Pluralistic Moral Gap: Understanding Judgment and Value Differences between Humans and Large Language Models

    cs.CL 2025-07 conditional novelty 6.0 of 10

    LLMs align with human moral judgments only under high consensus, concentrate on a narrow set of moral values, and the profile-based prompting method's reported improvement is evaluated in-sample.

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