EPSCoR Research Fellows: NSF: Obtaining Data Science Expertise to Support Evidence-based Policymaking for Public Lands Counties
- Institution
- Regents of the University of Idaho, ID
- Principal investigator
- Chelsea S Pennick
- Funding
- $179K
- Duration
- 2026–2027
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Abstract
This Research Infrastructure Improvement EPSCoR Research Fellows project provides a fellowship to a Research Assistant Professor at the University of Idaho. This work is conducted in collaboration with Dr. Tyler Scott and the Center for Environmental Policy & Behavior (CEPB) at University of California, Davis. CEPB is a research training center for quantitative social scientists from multiple graduate programs. The principal investigator (PI) will learn how to create and maintain a program for environmental policy and management research that scales across individual faculty and students. The PI will also draw from CEPB’s expertise in natural language processing and related data science skills. These tools will be applied to process and analyze novel data about the economics of public lands and local government budgets in the rural west. The novel dataset resulting from this project will provide infrastructure for new research on the impacts of public lands on local government financial conditions. Advances in data science, such as the use of large language models, present unrivaled opportunities to make unstructured data available to answer important questions. As part of this fellowship, the PI proposes to use natural language processing and transformer-based models (i.e., large language models) to collect, aggregate, and standardize local government financial reports and administrative records to build a dataset of local government fiscal decision variables, enabling the PI to explore new questions about the relationship between federal public lands and county-level fiscal outcome variables (tax and expenditure decisions). Further, the study will simultaneously investigate the factors that mediate or exacerbate these relationships including state factors (state-level aid, tax and expenditure restrictions) and local circumvention policies (special districts, redistribution policies). This fellowship will provide core research infrastructure for computational data science within the PI’s department and the data infrastructure to support the development of a Public Lands County Finance Center in conjunction with the University of Idaho’s Institute for Interdisciplinary Data Science. This project is supported by the EPSCoR Research Infrastructure Improvement Program: EPSCoR Research Fellows, which supports early- and mid-career investigators in eligible jurisdictions to develop collaborations at the nation’s private, government or academic research institutions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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