I study presidential power in the separation of powers system. My research asks what policy presidents pursue through unilateral actions, and how they calibrate the policy content of those actions under institutional constraint. To study this, I develop an anchored embedding scaling method to measure policy displacement of unilateral actions.
Substantively, I work on American political institutions, executive politics, legislative politics, and political communication. Methodologically, I use large-scale text analysis, large language models, and other computational tools to build new measures from political texts.
This dissertation examines how presidents calibrate the content of unilateral actions to manage institutional constraints and maximize political benefits. The first chapter proposes a new measurement framework, anchored embedding scaling (AES), to estimate the locations of unilateral actions within the liberal-conservative policy space. The second chapter recovers the inherited policy status quo for each directive and estimates the policy displacement produced by unilateral actions using AES. The third chapter extends existing models of unilateral action by proposing a cost-constrained model of presidential policy choice.
Chapter 1
Classification and AES
This chapter develops a classification scheme for unilateral actions and estimates the ideological positions of presidential directives. It classifies directives by policy content and instrument, then uses anchored embedding scaling to locate them in liberal-conservative policy space.
Chapter 2
Status Quo and Policy Displacement
This chapter recovers the policy status quo from which presidents depart when they act unilaterally. It develops a pipeline for identifying the relevant status quo against which presidential directives can be interpreted as policy movement. Using anchored embedding scaling, the chapter estimates the policy displacement produced by unilateral actions.
Chapter 3
Theory and Institutional Constraint
This chapter develops a cost-constrained model of unilateral action. It tests the theoretical prediction that presidents are likely to move policy toward the veto pivot, while arguing that presidents may stop short of this constraint boundary when institutional costs become sufficiently high. The chapter examines why presidents limit the policy displacement of unilateral actions even when unilateral authority allows them to move policy on their own.
My research brings together political institutions, political communication, and computational methods to study political actors' strategy and representation.
with Eunseong Oh
Develops a diagnostic of vocabulary separability to guide model selection in political text classification, and explains when sparse representations outperform dense alternatives.
Text Analysis · NLP · Model Selection
Examines whether conspiracy beliefs shape campaign contributions in American politics, using the 2012 and 2016 ANES. The paper links conspiratorial belief to donor behavior across partisan contexts.
Campaign Finance · Conspiracy Theories · ANES
with Eunseong Oh
Presented at PolMeth 2025
Uses CLIP technology to analyze how political videos portray megadonors. Applies multimodal deep learning to understand the framing of political visual media.
Multimodal Analysis · CLIP · Political Communication
Teaching Assistant — UC Riverside (2023 – Present)
Vocabulary separability diagnostics for text classification.
Companion package for Oh and Yu, “When Sparse Beats Dense: Vocabulary Separability and Model Selection in Political Text Analysis.”