A Few Words on Topics and Approaches
My research sits at the confluence of public policy, statistics, and data science. Substantively, I conduct research on public policy, bureaucracy, regulation, and agenda setting. Most of my work involves text-as-data and focuses on the content of elite policy documents and how they are used in policymaking. Conceptually, I am interested in how governing and policy systems produce, process, and use information and how this connects to adaptiveness and resiliency. The projects below focus on the interaction of bureaucracies and interest groups in policymaking.
Most of these projects involve the classification of text for which I use the tools of the data scientist - scraping the web, machine learning, and data visualization. From these large data sets, I build statistical models that illustrate how information matters in public policy and test inferences about how governing and policy systems process information, prioritize issues, and set the policy agenda.
Education Policy: This project examines the interest group argumentation and the use of research in education policy. In particular, we examine how organizations structure the supply and use of information. It is funded by the National Science Foundation (NSF) and inolves the collection and coding of ~100k public comments provided on regulatory proposals in education. These are parsed by paragraph and coded for substantive topic, data provided, and citations of research. The projects description can be found at the NSF. Collaborators: Deven Carlson, University of Oklahoma
- Regulatory Complexity & Democratic Deliberation: We examine how regulatory complexity shapes the quantity and quality of democratic deliberation in regulatory policy on GMOs. We examine topical and scientific complexity and relate it to the analytic and social process of democratic deliberation. The empirical foundation of the project is a data set of 800 comments on 75 regulatory proposals. Collaborators: Justin Reedy, University of Oklahoma
- Food Security & SNAP: This research addresses how information influences the development of policy frames and problem definitions in food security, and specifically, the Supplemental Nutrition Assistance Program (SNAP) in the United States. Using public comments on regulatory proposals, we scrape public and interest group comments on 522 SNAP proposed and final rules. We use machine learning to topically analyze the text of comments and assess the degree to which larger, boundary-spanning policy problems have redefined argumentation and conflict around SNAP. The analysis also allows us to understand how the emergence of boundary-spanning problems like climate change, social inequality, and public health have influenced the way various actors have come to define the problem of food security. Collaborators: Clare Brock, Texas Woman’s University
Methods of the Policy Process: The goal of this edited volume is one of communication of the diverse ways that theoretically-based research in public policy is conducted and move toward better methods of the policy process. The chapters address the methodological approaches common within theoretical traditions in public policy, but also push onward to new frontiers in conceptualization, measurement, and statistical modeling.
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