Reading the High Court at a Distance: Topic Modelling the Legal Subject Matter And Judicial Activity of the High Court of Australia, 1903–2015

In this article we apply the method of quantitative textual analysis known as ‘topic modelling’ to a significant Australian legal text corpus: that of judgments of the High Court of Australia from 1903 to 2015. The High Court of Australia has been a perennial topic for study and analysis. It is the highest court in the Australian judicial hierarchy and the site of many of the most significant contests of legal doctrine and practice in Australian history. We find that the topic models generated by this research enable the development of a range of unique, novel and robust observations of the High Court’s judicial workload and the shifting make-up of its legal subject matter over time. Moreover, this article reveals the feasibility and value of topic modelling as a method for the study of legal texts and practices that might fruitfully complement other methods of legal scholarship

Critical legal reading: The elements, strategies and dispositions needed to master this essential skill

Alex Steel, Kate Galloway, Mary Heath, Natalie Skead, Mark Israel, Anne Hewit

Neural Discourse Structure for Text Categorization

We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization. Our approach uses a recursive neural network and a newly proposed attention mechanism to compute a representation of the text that focuses on salient content, from the perspective of both RST and the task. Experiments consider variants of the approach and illustrate its strengths and weaknesses.Comment: ACL 2017 camera ready versio

Understanding the Power of Framing: The Role of Policy Context and Stakeholder Characteristics in the EU Feedback Mechanism

Postponed access: the file will be accessible after 2023-07-01MasteroppgaveSAMPOL350MASV-SAP

Topic Modeling and Text Analysis for Qualitative Policy Research

This paper contributes to a critical methodological discussion that has direct ramifications for policy studies: how computational methods can be concretely incorporated into existing processes of textual analysis and interpretation without compromising scientific integrity. We focus on the computational method of topic modeling and investigate how it interacts with two larger families of qualitative methods: content and classification methods characterized by interest in words as communication units and discourse and representation methods characterized by interest in the meaning of communicative acts. Based on analysis of recent academic publications that have used topic modeling for textual analysis, our findings show that different mixed‐method research designs are appropriate when combining topic modeling with the two groups of methods. Our main concluding argument is that topic modeling enables scholars to apply policy theories and concepts to much larger sets of data. That said, the use of computational methods requires genuine understanding of these techniques to obtain substantially meaningful results. We encourage policy scholars to reflect carefully on methodological issues, and offer a simple heuristic to help identify and address critical points when designing a study using topic modeling.Peer reviewe