Reading Law's Great Unread: Qualitative Computational Methods, Artificial Intelligence and the New Empirical Legal Research

How will new computational technologies change legal research and our visions of what law is? Inspired by the work of digital humanists, Bourdieu, and sociologists of literature, this dissertation explores how the methods of “distant reading” can be used to develop new classes of critical insights about law. After situating the project theoretically, this dissertation reports on a series of new computational studies about Canadian law. Chapter 1 measures Canadian statutory and regulatory law, showing that law has grown unevenly over the past decade and a half. Chapter 2 uses new artificial intelligence to transcribe and analyze Supreme Court of Canada hearings, revealing gendered and linguistic speaking patterns among justices. Chapter 3 shows how computational methods can be deployed to detect inconsistency and discord in a jurisprudence, in this case Canada’s law of terrorism. Chapter 4 uses machine learning to study refugee law jurisprudence, particularly showing how it has developed over the past decade. Chapter 5 leverages new computational techniques to analyze Social Security Tribunal of Canada decisions regarding employment insurance appeals and suggests that new computational analyses might usefully change legal education. It concludes by considering how some visions of computational legal analysis—despite the sweep and scope of their projects—are part of old and traditional visions of what law is.

Author dc:creator

• Wallace, Simon

Advisor dc:contributor.advisor

• Rehaag, Sean

Statement dc:rights

• Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.

Language dc:language

en

Handle dc:identifier.uri

https://hdl.handle.net/10315/42833

OAI identifier oai:identifier

oai:yorkspace.library.yorku.ca:10315/42833