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University of Texas – Austin

1. -2081-2407. Power in text : extracting institutional relationships from natural language.

Degree: Government, 2018, University of Texas – Austin

How do legislators allocate policy-making authority? At least in the legal context, distribution-of-power arrangements are usually articulated in written documents. Unfortunately, extracting these relationships is difficult, leading scholars to restrict themselves to studies of single policy areas or to a small set of high-visibility laws. In this project, I address this limitation through a neural network-based approach that extracts power relationships from legal language in a scalable, valid fashion. I then apply this approach to study institutional design in enacted US legislation. Substantively, I demonstrate that policy preferences of executive and legislative actors exert surprisingly little influence on formal institutional design choices. For all but the most politically salient laws, implementation arrangements are structured by the policy area and issue under consideration rather than elite political preferences. This argument - which would not have been possible to test without the measurement tools I develop - highlights both the importance of the tools I develop and the need for scalable measurement techniques in political science. Advisors/Committee Members: Elkins, Zachary, 1970- (advisor), Jones, Bryan (committee member), Jessee, Stephen (committee member), Wilkerson, John (committee member).

Subjects/Keywords: Public law; Legislative studies; Methodology; Text analysis; Supervised machine learning

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APA (6th Edition):

-2081-2407. (2018). Power in text : extracting institutional relationships from natural language. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68624

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-2081-2407. “Power in text : extracting institutional relationships from natural language.” 2018. Thesis, University of Texas – Austin. Accessed January 15, 2019. http://hdl.handle.net/2152/68624.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-2081-2407. “Power in text : extracting institutional relationships from natural language.” 2018. Web. 15 Jan 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-2081-2407. Power in text : extracting institutional relationships from natural language. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Jan 15]. Available from: http://hdl.handle.net/2152/68624.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-2081-2407. Power in text : extracting institutional relationships from natural language. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68624

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

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