This 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. We are looking for a passionate, curious, and careful candidate with data science and programming skills, and 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!
Image source: Marta Manso / Wikipedia
Project Overview
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 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.
Aims
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).
Project Partners
Project supervisors:
- First supervisor: Dr. Jon Reades, Department of Geography, King’s College London
- Second supervisor: Assoc. Prof. Maria Liakata, Department of Computer Science, The Alan Turing Institute/University of Warwick
- Industry supervisor: Rachael Kotarski, British Library.
Ethical Considerations
This work raises novel ethical issues because of its methods and outputs: gender is not considered ‘sensitive’ personal data, but the inference of such characteristics is nonetheless untested by the GDPR. The project will also raise, and must actively engage with, the ethics of drawing conclusions based on the inference of gender from input features—in particular, the cumulative and uncertain impact that the presence of false positives and negatives implies—but this represents an important opportunity for wider engagement with the limits of ‘ethical AI’.
We recognise that this project tackles just one aspect of a much larger problem: the challenges faced by BME and LGBTQ+ students, and the intersectional challenges encountered by, for instance, black women in academia, are profound, but the scale and technical complexity of this issue lies beyond the scope of a single PhD. The focus on women in this work therefore represents only a first step, but it is hoped that successes here will lead to follow-on work engaging more widely with ‘Research and Innovation Culture’ (UKRI Delivery Plan 2019) and ‘Talent, methods and leadership’ (ESRC Delivery Plan 2019).
Person Description & Application
You must be eligible for ESRC funding (UK or EU only) and will be asked to pursue either the 1+3 (MSc+PhD) or +3 (PhD only) track. Note: you are eligible for the 1+3 track even if you already have a first Masters degree in an unrelated discipline.
If you are pursuing (or are asked to pursue) the 1+3 option, then you will need to apply for an appropriate programme (e.g. King’s MSc Urban Informatics) prior to beginning your doctoral research. The cost of the MSc is included in the ESRC award. Depending on your background, in order to comply with ESRC funding rules regarding Core Training Requirements we may require you to take the Theorising Big Data module (or an agreed equivalent) as part of your MSc and/or to complete specific short-courses in the social sciences during the first year of your PhD.
Whatever your background, you will be able to provide evidence of your existing technical competency level(s) as part of the selection process; however, we are also looking for a student who is passionate about the questions that this research seeks to tackle: this is not a purely technical ‘data science’ exercise.
We expect that the successful applicant will most likely have developed their skills and interests through one of the three following pathways:
Pathway 1: Computer Science, Statistics, or Bioinformatics
You will have an undergraduate degree (or first MSc) in a discipline that involved substantial programming and statistical analysis, but you are interested in applying your knowledge to pressing social science questions and to messy, real-world data.
Pathway 2: Quantitative Social Science (e.g. Economics or Quantitative Geography)
You will have an undergraduate degree (or first MA/MSc) in a social science discipline that involved substantive programming and statistical analysis, but require training in more advanced data science techniques. Alternatively, you might have learned to code ‘on the side’ and used scripts as part of your undergraduate or Masters dissertation but, again, require formal training in data science techniques.
Pathway 3: Mix of Academic & Professional Experience
We would welcome applicants with relevant professional experience equivalent to a Masters-level degree. In particular, if your background includes both the necessary computer science/programming background and at least some postgraduate training in the social sciences (or relevant work experience), it may be possible to apply directly to the PhD (+3 only) and to undertake specific ‘top up’ training in the first year of the PhD.
Application Deadline & Requirements
In order to give applicants more time, we have set a new application deadline of 23:59 on Friday 21 February 2020.
Please pay careful attention to the eligibility requirements of the ESRC as we are unable to make exceptions to these rules. We are not able to offer exceptions to the residential requirements.
To apply, you should follow the guidance for LISS DTP Studentship applicants Steps 1 (check eligibility), 2 (decide on 1+3 or +3), and 5 (LISS DTP form) but please send these documents to Dr. Reades directly along with the following information:
- A copy of your CV highlighting relevant study and work experience.
- A completed ESRC LISS DTP Collaborative (CASE) Application form (please do not include sensitive information at this time).
- A ‘Personal Statement’ of no more than 2 pages detailing your interest in the project and highlighting relevant skills; this may include personal, academic, and professional reasons.
- The names of 2 referees able to speak to your suitability for this project (we will only contact those of the successful applicant).
- Transcript(s) for all relevant degrees.
These will be reviewed by the supervisory team and at least one external reviewer who is not associated with the project.
Please feel free contact Dr. Jon Reades with any questions.
King's Geocomputation 
