Computational Methods and Qualitative Data: Trailblazing Women and the Law

Abstract

This thesis examines the utility of computational methods as applied to a qualitative data set arising from full life, oral history interviews. The overarching question proposed is: ‘What can computational methods, applied to a qualitative data set arising from full life oral histories, add to our understanding of the lives and networks of Australia’s trailblazing women lawyers?’ The nature of this topic dictates the use of selected software programs and a comparative analysis of the usefulness of these software programs for effective data interrogation.

The project was developed experimentally and experientially to test the hypothesis that through the application of computational methods to qualitative data, researchers can learn more about data than would be possible through the more human centric analytical methods traditionally employed by the humanities and social sciences.

The project has utilsed a grounded theory approach, applying this to the data arising from full life oral history interviews with sixteen of Australia’s trailblazing women lawyers. Internationally, the use of oral history to uncover the biographical and cultural history of trailblazing women lawyers is established. However, in Australia, empirical enquires have erased biographical details and neglected the storytelling element of oral history. Women lawyers stand at the professional forefront of women’s participation in Australian civic life. The last 100 years has seen many new women pioneers at the frontier of the Australian legal profession, as they enter previously male-only areas of practice, adopt new ways of practicing, take up elite legal positions and enter the profession from increasingly diverse backgrounds. The majority of the women included in this study are not mentioned in any public record, thereby limiting the historical picture of women’s experiences upon first entering the legal profession. This project seeks to fill that gap by providing a holistic picture of the lives of the trailblazer, through their individual and shared networks.

Iterative strategies are utilised and the project methodology necessitated working back and forth between the data and developing analysis, whilst utilising comparative methods. The hypothesis is that through continually re-engaging with the data, further research questions arise and can then be explored. Further questions, crucial to answering the overall question posed in this thesis, include:

• What can database technology including the relational database, the Online Heritage Resource Manager (OHRM) and graph database Neo4j, tell us about the lives of the trailblazers? • How does qualitative data representation (in this thesis, the addition of network visualisation through ConneX) add value to research outcomes?

Each of these questions assists in determining the added value of the computational approach. This is the key goal of this project as it seeks to identify what this method of research enables us to illuminate about this particular group of trailblazing women lawyers.

The conclusions of this project confirm the assertion that computation helps to reveal patterns within the lives of this select group of trailblazing women lawyers. Quantitative data, including the total number of ‘nodes,’ or ‘entities’ and relationship counts were explored, as was extensive qualitative data, including such shared concepts as ‘experience of discrimination’ and ‘periods living overseas.’ Furthermore, the query, ‘shortest path,’ as discussed in relation to the establishment of graph theory, revealed the most frequently occurring ties shared across the set of trailblazers as a whole. The three key shared key findings amongst this particular group included having attended a single-sex school, access to mentors and participation in a professional association, in this case, the Women Lawyers Association. To this end, it is recommenced that socio-legal and socio-scientific researchers seek to incorporate digital tools to help curate, explore and analyse qualitative data.