This ESRC-funded PhD project seeks to employ data science and text mining methods to enhance our understanding of how a ‘gendering’ of the research pipeline might offer insight into the challenges faced by women as they make the transition from students to independent researchers. This is an interdisciplinary project suited to students who want to bridge computer science/statistics and the social sciences, and we are looking for a passionate, curious, and careful candidate with an interest in NLP and the ethics of data science/AI to work on an exciting collaborative CASE Studentship involving the British Library and supervisors at King’s College London and the Alan Turing Institute/University of Warwick!
In 2018, women made up 57.8% of taught postgraduates, 44.6% of research postgraduates, 39.8% of non-professorial academic staff, and 20.2% of professorial staff in Science, Engineering and Technology (SET) disciplines (Equalities Challenge Unit 2019a,b). In other words: women remain systematically under-represented in academia, with fewer progressing from PhD to Professor than their (overwhelmingly white) male colleagues.
We know a bit (though not nearly enough) about the kinds of negative personal experiences that drive women out of academia, and we have useful snapshots of the overall composition of the academic workforce at the institutional and, to a lesser extent, disciplinary levels. However, we know next-to-nothing about the research environment formed by the conjunction of discipline, institution, and department, and of how this shapes doctoral research and researchers.
This project seeks to derive critical input features such as gender, discipline, and department from potentially incomplete and ‘messy’ data held by the British Library so as to develop a model of the contributions made by each of these levels to the research careers of women in academia. Focussing on this intellectual and academic ‘geography’ will help to develop new lines of inquiry, while the use of British Library data spanning decades and disciplines counters an important evidentiary gap.
This project therefore seeks to gender of the pipeline of PhD ‘talent’ into the university sector, and the aims of this research include:
• To measure the gender composition of disciplines, institutions, and departments over time; and
• To measure the interaction between these scales and their consequences for observed inequalities; and
• To explore how subtle, indirectly measured factors might reinforce differences in research practice.
With this, we hope to arrive at a deeper understanding of the ways in which, as Valian puts it: “…mountains are molehills, piled one on top another over time” (2005, p.210).
Please see our blog post advertising the project for additional detail about the project, person specification, and application process: https://kingsgeocomputation.org/2019/12/16/grep/