Dissertation Proposal: Agnes Namyalo

Title: A Prompt-Engineered LLM Framework for Extracting ESA Narratives with Narrative Policy Framework-Based Coding, Tracing Their Movement Between News Media and Congress, and Examining Their Association with Regulatory Timing

Program: Computing Ph.D.

Committee Chair: Matt Williamson

Committee: Matt Williamson, Francesca Spezzano, Chris Birdsall

Abstract:

Biodiversity loss and species extinction remain urgent environmental challenges, making effective species-protection policy increasingly important in the United States. Yet the implementation and public understanding of such policy are shaped not only by scientific and legal evidence, but also by how endangered species issues are communicated and contested across media and political institutions. In the case of the U.S. Endangered Species Act (ESA), these public debates are shaped not only by scientific and legal information but also by narratives that define problems, assign responsibility, and advocate policy responses. These narratives are distributed across large collections of news and congressional text, making text analysis difficult with manual coding alone. This dissertation develops a computational framework for examining how ESA narratives are constructed, circulate between media and political institutions, and relate to policy outcomes. I pursue this goal through three objectives. Objective 1 develops and validates a prompt-engineered large language model (LLM) pipeline for extracting Narrative Policy Framework (NPF) elements from national news and Congressional Record text. Objective 2 uses the resulting extracted narratives to examine directional and reciprocal agenda-setting relationships between news coverage and congressional attention across multiple temporal lag windows and publication outlets. Objective 3 links media attention and narrative characteristics to the timing of formal ESA decisions using survival analysis of species listing and critical habitat actions. Together, the three objectives move from measurement to cross-institutional narrative agenda setting, to policy consequences, providing an integrated computational approach to the study of environmental policy discourse.