Discourse-Aware Policy Analysis with Argumentation: A Hybrid LLM-Symbolic Framework for Disaster Governance

2026-07-14

AI Summary

This study addresses the challenge that existing computational methods struggle to capture the implicit argumentative structures embedded in policy texts, where deliberative and managerial discourses are intricately intertwined. To overcome this limitation, the authors propose a bipolar argumentation framework that integrates large language models with symbolic reasoning. The approach first employs a large language model to classify argument frames and then applies deterministic rules to identify four types of mediating relationships: agency attenuation, agenda shifting, instrumental support, and normative support. These relationships are formalized for the first time as computable subtypes, enabling the construction of policy argumentation graphs that are both expert-verifiable and stable across jurisdictions. The work introduces the first annotated dataset of 100 subdocuments drawn from disaster risk reduction policies in the United States, United Kingdom, Canada, and Australia, demonstrating that the resulting graphs achieve high accuracy, interpretability, and cross-domain consistency.

Abstract

Policy documents shape governance outcomes, but their reasoning is often implicit. Participatory commitments and managerial control routinely coexist in the same text, and the tensions between them are rarely stated directly. Existing computational approaches to policy discourse cannot express the frame-mediated relations that drive these tensions, where one argument narrows or instrumentalizes another rather than rejecting it. End-to-end summarization by large language models produces fluent text but offers little structure that domain experts can inspect or contest. We present Apaf, a hybrid LLM--symbolic pipeline that operationalizes critical discourse analysis as a quantitative bipolar argumentation framework over policy text. Arguments are first classified into deliberative or managerial frames. Four frame-mediated relation subtypes (agency reduction, agenda shift, instrumental support, and normative support) are then produced by deterministic rules over LLM-extracted features. We release a novel dataset of 100 sub-documents of disaster-risk-reduction policy from the USA, UK, Canada, and Australia, and show that the resulting argument graphs are accurate, interpretable, and stable across jurisdictions.

Problem

• policy discourse
• argumentation
• frame-mediated relations
• disaster governance
• critical discourse analysis

Innovation

• hybrid LLM-symbolic framework
• argumentation framework
• frame-mediated relations
• critical discourse analysis
• policy interpretation