Power in text : extracting institutional relationships from natural language

Simple item page
dc.contributor.advisorElkins, Zachary, 1970-
dc.contributor.committeeMemberJones, Bryan
dc.contributor.committeeMemberJessee, Stephen
dc.contributor.committeeMemberWilkerson, John
dc.creatorShaffer, Robert Bradley
dc.creator.orcid0000-0002-2081-2407
dc.date.accessioned2018-10-01T20:25:23Z
dc.date.available2018-10-01T20:25:23Z
dc.date.created2018-08
dc.date.issued2018-09-14
dc.date.submittedAugust 2018
dc.date.updated2018-10-01T20:25:23Z
dc.description.abstractHow 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.
dc.description.departmentGovernment
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T2NK36Q39
dc.identifier.urihttp://hdl.handle.net/2152/68624
dc.language.isoen
dc.subjectPublic law
dc.subjectLegislative studies
dc.subjectMethodology
dc.subjectText analysis
dc.subjectSupervised machine learning
dc.titlePower in text : extracting institutional relationships from natural language
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentGovernment
thesis.degree.disciplineGovernment
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
SHAFFER-DISSERTATION-2018.pdf
Size:
3.97 MB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
4.45 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.84 KB
Format:
Plain Text
Description:
Download

Collections

UT Electronic Theses and Dissertations
Department of Government Theses and Dissertations