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Running With Scissors? Integrating GPT Models Into Public Policy Research

·627 字·2 分钟
Polihot 公共政策 研究方法 LLM相关 Large Language Models GPT Public Policy Text Annotation Advocacy Coalition Framework
PSJ — 2026-06-23 Original Running With Scissors? Integrating GPT Models Into Public Policy Research Giulia Mariani, Allegra H. Fullerton Abstract ABSTRACT The integration of large language models (LLMs) into public policy research presents both exciting opportunities and methodological challenges. This research note explores how OpenAI's GPT can be used to semi‐automate the annotation of legislative testimony within the Advocacy Coalition Framework, focusing on emotion‐belief dyads. Building on Emotion‐Belief Analysis, we demonstrate how GPT can assist in identifying these complex constructs under human supervision. Our contributions are threefold: (1) we provide practical guidance for applying LLMs to publicly available textual data, (2) we propose a semiautomated workflow that strengthens conceptual clarity, transparency, consistency, replicability, and accessibility, and (3) we reflect on the ethical and methodological implications of LLM‐assisted research. As LLMs continue to advance, this research note aims to help scholars balance innovation with rigor and integrate these tools responsibly into policy research, offering lessons that extend to the study of frames, discourses, narratives, and other ideational dimensions of policymaking.

Policy Learning: Mapping the Conceptual Minefield

·771 字·2 分钟
Polihot 公共政策 公共行政 Policy Learning Conceptual Clarity Public Policy Public Administration Knowledge Utilization
PSJ — 2026-06-21 Original Policy Learning: Mapping the Conceptual Minefield Bishoy Zaki Abstract ABSTRACT Policy learning has become a central and widely deployed lens in public policy and administration research. Yet policy learning's success has also produced paradoxical effects. Decades of scholarship have generated a dense conceptual terrain populated by numerous constructs and learning descriptors that are often unclearly positioned and frequently collapsed into the broad label of “learning types.” The result is persistent conceptual ambiguity that limits comparability and hinders cumulative knowledge production, leaving policy learning widely experienced as a “minefield” of overlapping constructs. This article addresses that problem by mapping the policy learning terrain through building an Archimedean device that develops four construct categories, forms , modes , mechanisms , and types of learning , anchored in two reference points: the occurrence of learning, defined as the deliberate pursuit and processing of policy‐relevant knowledge by policy actors, and learning outcomes, meaning what learning produces, ranging from stability to varying degrees of change. It maps a selected set of prominent constructs into these categories to provide a construct‐identity crosswalk and shared analytic grammar that support clearer specification, more defensible operationalization, and the space for more consistent cross‐study synthesis without erasing theoretical pluralism, and that can be layered onto existing learning frameworks for additional analytical leverage and theory development.

Regional News, Regional Bias? Evidence From Media Discourses and Welfare Decisions in Germany

·822 字·2 分钟
Polihot 公共政策 公共行政 Regional Media Street-Level Bureaucracy Welfare Decisions Immigration Germany
PSJ — 2026-06-08 Original Regional News, Regional Bias? Evidence From Media Discourses and Welfare Decisions in Germany Stefanie Rueß Abstract ABSTRACT How do media representations of immigrants shape their treatment by street‐level bureaucrats? Despite a uniform federal legal framework, decision‐making varies substantially across local welfare offices. Though prior research links national news reporting and policy implementation, little is known about how regional variation in news reporting on immigration is linked to spatial variation in bureaucratic decision‐making. This is a key gap because regional media structures how residents perceive local developments and problems. I argue that street‐level bureaucrats are embedded within such a regional news environment, where reporting shapes their perception of regional immigration patterns and makes stereotypes cognitively accessible to them, influencing their decisions. I term this dynamic the regional media bias mechanism . To examine this phenomenon, I combine state‐level data on benefit reduction rates in Germany’s welfare program Citizen’s Benefit with regional newspaper articles (2010–2019). Leveraging topic modeling and panel data analysis, I show that regional narratives on rather positive topics of immigration are associated with more favorable administrative outcomes for immigrants, whereas frames emphasizing rather negative aspects correspond with stricter treatment. These results highlight the critical role of regional media in explaining regional variation in administrative decision‐making, thereby shaping policy implementation.