Public Policy

PSJ 54/3 2026-08-07 Original Supply‐Side Policy Feedback Isabel M. Perera Abstract ABSTRACT The scholarship on policy feedback has been among the most plentiful and successful of the policy sciences. Much of it has focused on the demand‐side, or on the political relationship between a public policy and its recipients. Less attention has been paid to the supply‐side: the political relationship between a public policy and its providers. Parents may care about education, patients about health care, and prisoners about criminal justice—but an emerging literature shows that teachers, hospital employees, and the police care just as, if not more, about these areas. This article builds on new work to introduce a theory of supply‐side policy feedback and outline a research agenda for its further investigation.

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.

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.