Epistemic orientation predicts legislative effectiveness among members of the US Congress
- URL: http://arxiv.org/abs/2609.11865v1
- Date: Thu, 10 Sep 2026 17:41:54 GMT
- Title: Epistemic orientation predicts legislative effectiveness among members of the US Congress
- Abstract summary: Evidence-oriented language in U.S. congressional floor speeches has declined since the mid-1970s.<n>This study shifts the analysis from congressional sessions to individual members of Congress.
- Score: 0.5607632629560914
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Truth and evidence-based communication provide important foundations for democratic governance, accountability, and collective decision-making. Prior work shows that evidence-oriented language in US congressional floor speeches has declined since the mid-1970s, alongside broader changes in legislative productivity and polarization. This study shifts the analysis from congressional sessions to individual members of Congress to examine whether epistemic orientation varies systematically across legislators and whether it relates to political behavior and legislative effectiveness. Using the Evidence-Minus-Intuition (EMI) score, we measure the relative prevalence of evidence-oriented versus intuition-oriented language in congressional floor speeches and Twitter posts. We link these measures to legislator-level data on ideology, institutional position, communication context, and Legislative Effectiveness Score (LES). The results show that more ideologically extreme members use less evidence-oriented language on the congressional floor. EMI also exhibits cross-platform consistency with members who use more evidence-oriented language in floor speeches also being more evidence-oriented on Twitter, although EMI is lower on Twitter overall. Finally, EMI in congressional speeches is positively associated with individual legislative effectiveness, even after accounting for ideology and extensive political, institutional, demographic, topical, and communication volume controls. These findings suggest that evidence-oriented language is not only an aggregate feature of congressional discourse but also a meaningful attribute of individual-level legislative communication and effectiveness.
Related papers
- Politics of Feelings: Emotional Expression and Legislative Effectiveness in the U.S. Congress [0.0]
We measure eight discrete emotions: anger, fear, disgust, sadness, joy, enthusiasm, pride, and hope.<n>We find that congressional speeches are becoming emotionally expressive over time.<n>The relationship between emotional expression and legislative effectiveness depends on the specific emotions expressed.
arXiv Detail & Related papers (2026-09-09T14:03:50Z) - Epistemic orientation in parliamentary discourse is associated with deliberative democracy [0.5607632629560914]
We introduce a scalable approach to measure epistemic orientation using the Evidence--Minus--Intuition ( EMI) score.<n>We apply this approach to 15 million parliamentary speech segments spanning 1946 to 2025 across seven countries.<n>We find that EMI is positively associated with deliberative democracy within countries over time, with consistent relationships in both contemporaneous and lagged analyses.
arXiv Detail & Related papers (2026-04-21T17:19:09Z) - ParliaBench: An Evaluation and Benchmarking Framework for LLM-Generated Parliamentary Speech [0.2446948464551684]
Parliamentary speeches require not only linguistic quality but also political authenticity and ideological consistency.<n>Current language models lack specialized training for parliamentary contexts.<n>We present ParliaBench, a benchmark for parliamentary speech generation.
arXiv Detail & Related papers (2025-11-11T13:43:09Z) - Latent Topic Synthesis: Leveraging LLMs for Electoral Ad Analysis [51.95395936342771]
We introduce an end-to-end framework for automatically generating an interpretable topic taxonomy from an unlabeled corpus.<n>We apply this framework to a large corpus of Meta political ads from the month ahead of the 2024 U.S. Presidential election.<n>Our approach uncovers latent discourse structures, synthesizes semantically rich topic labels, and annotates topics with moral framing dimensions.
arXiv Detail & Related papers (2025-10-16T20:30:20Z) - HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation [67.69631485036665]
We conduct a comprehensive examination of hate speech regulations and strategies from three perspectives.<n>Our findings reveal significant inconsistencies in hate speech definitions and moderation practices across jurisdictions.<n>We suggest ideas and research direction for further exploration of a unified framework for automated hate speech moderation.
arXiv Detail & Related papers (2025-07-06T11:25:23Z) - KOKKAI DOC: An LLM-driven framework for scaling parliamentary representatives [0.0]
This paper introduces an LLM-driven framework designed to accurately scale the political issue stances of parliamentary representatives.<n>By leveraging advanced natural language processing techniques and large language models, the proposed methodology refines and enhances previous approaches.<n>The framework incorporates three major innovations: (1) de-noising parliamentary speeches via summarization to produce cleaner, more consistent opinion embeddings; (2) automatic extraction of axes of political controversy from legislators' speech summaries; and (3) a diachronic analysis that tracks the evolution of party positions over time.
arXiv Detail & Related papers (2025-05-11T21:03:53Z) - Aligning AI with Public Values: Deliberation and Decision-Making for Governing Multimodal LLMs in Political Video Analysis [48.14390493099495]
How AI models should deal with political topics has been discussed, but it remains challenging and requires better governance.<n>This paper examines the governance of large language models through individual and collective deliberation, focusing on politically sensitive videos.
arXiv Detail & Related papers (2024-09-15T03:17:38Z) - Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
This study seeks to identify and quantify biases in simulating political samples with Large Language Models.
Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
arXiv Detail & Related papers (2024-07-16T05:52:26Z) - Computational analysis of US Congressional speeches reveals a shift from evidence to intuition [1.5590818387626204]
Pursuit of honest and truthful decision-making is crucial for governance and accountability in democracies.
We explore a continuum of perspectives from evidence-based reasoning, rooted in ascertainable facts and data, at one end, to intuitive decisions that are driven by feelings and subjective interpretations, at the other.
We analyze the linguistic traces of those contrasting perspectives in Congressional speeches from 1879 to 2022.
arXiv Detail & Related papers (2024-05-12T16:17:41Z) - DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
We introduce DELTA, a discriminative model designed for legal case retrieval.
We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability.
Our approach can outperform existing state-of-the-art methods in legal case retrieval.
arXiv Detail & Related papers (2024-03-27T10:40:14Z) - The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings [0.0]
The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment.
The paper additionally introduces the first domain-specific multilingual transformer language model for political science applications.
arXiv Detail & Related papers (2023-09-18T14:01:06Z) - UniSpeech: Unified Speech Representation Learning with Labeled and
Unlabeled Data [54.733889961024445]
We propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data.
We evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus.
arXiv Detail & Related papers (2021-01-19T12:53:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.