Essays on machine learning and empirical legal studies
Abstract/Contents
- Abstract
- This dissertation is a collection of essays using supervised machine learning to measure the prevalence of textualism in judicial opinions. The first essay compares my supervised machine-learning measures to existing natural language processing methods. The second and third essays explore what these measures tell us about the evolution of textualism in the federal courts of appeals and state supreme courts, respectively.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Peters, Austin |
|---|---|
| Degree supervisor | Bonica, Adam |
| Thesis advisor | Bonica, Adam |
| Thesis advisor | Cain, Bruce |
| Thesis advisor | Nyarko, Julian |
| Degree committee member | Cain, Bruce |
| Degree committee member | Nyarko, Julian |
| Associated with | Stanford University, School of Humanities and Sciences |
| Associated with | Stanford University, Department of Political Science |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Austin Peters. |
|---|---|
| Note | Submitted to the Department of Political Science. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/gx958zw0264 |
Access conditions
- Copyright
- © 2023 by Austin Peters
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