Scottish Graduate School of Social Science

Sgoil Cheumnaichean Saidheans Sòisealta na h-Alba
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Scottish Graduate School of Social Science

Sgoil Cheumnaichean Saidheans Sòisealta na h-Alba

Studentship opportunity

All Studentship Opportunities

The Framing of Corruption in Defence and National Security Policy: A Cross-National Analysis, 1987–2024

This studentship is funded by the ESRC through the Scottish Graduate School of Social Science (SGSSS)

Institution
University of Edinburgh
Pathway
Securities: Justice, Economies and Conflict
Studentship
Open Collaborative
Mode of study

Full-time / Part-time

Application deadline
4pm, 14 April 2022
Applications are due 24 March 2026, 5 pm GMT and
References are due 26 March 2026, 5 pm GMT

Project details

Global defence expenditure reached $2.7 trillion in 2024, marking a 9.4% annual increase—the steepest since the Cold War (SIPRI, 2025). As military budgets expand amidst rising geopolitical tensions, corruption risk in the defence sector represents a critical governance challenge with direct security consequences. Research demonstrates that corrupt defence institutions undermine state legitimacy, divert resources from capability development, and create grievances that violent non-state actors exploit. Yet, while extensive literature examines the empirical effects of corruption, no systematic analysis exists of how states discursively construct corruption as a security threat within national defence policy.

This research provides the first computational analysis of corruption framings across 119 countries’ national security and defence documents (1987–2024), examining how policy discourse shapes institutional responses to defence sector governance failures. Using natural language processing, network analysis, and machine learning on the Edinburgh National Security and Defence Documents Dataset (607 documents, 19 million words), the project investigates three questions: How do corruption-security framings evolve temporally? What factors explain cross-national variation in framing approaches? How do specific framings correlate with measurable policy outcomes?

The study employs sequential mixed-methods: large-scale computational text analysis identifies framing patterns, followed by structured focused comparison of 12 countries and process tracing of 3–4 intensive cases linking discourse to institutional reform. Collaboration with Transparency International Defence & Security provides access to expert insight and Government Defence Integrity Index data covering 86 countries, enabling validation of relationships between framing strategies and corruption control effectiveness. This partnership will ensure findings directly inform evidence-based anti-corruption advocacy whilst generating unprecedented analytical depth through methodological triangulation between discourse analysis and governance assessment.

Collaborative Partner: Transparency International: Defence and Security

About the University

The student will be based in the Politics and International Relations subject area in the School of Social and Political Science, which received the highest possible REF2021 rating for research environment. The School offers advanced computing resources for large-scale text analysis, close links with policy networks through initiatives like PeaceRep, and comprehensive research training through the School’s Research Training Centre. SPS hosts approximately 350 postgraduate research students with structured annual review processes to ensure problem identification and timely completion.

Eligibility

Applicants must meet the following eligibility criteria:

  • Applicants must hold or be predicted a First or a good 2:1 undergraduate degree in the social sciences, or have relevant comparable experience.
  • Applicants can have a Masters degree, however this is not a requirement.
  • The applicant must also show demonstrable interest in the topic area under investigation.
  • Applicants can study part-time or full-time

Funding

As per guidance published by UKRI in October 2020, a maximum of 30% of all studentships awarded can be made to international students, with the remaining 70% going to home students. 

Residential Criteria

To be classed as a home student, applicants must meet the following criteria:

  • Be a UK national (meeting residency requirements), or
  • Have settled status, or
  • Have pre-settled status (meeting residency requirements), or
  • Have indefinite leave to remain or enter.

For more on the UKRI eligibility guidance, click here

As per guidance published by UKRI in October 2020, a maximum of 30% of all studentships awarded can be made to international students, of which SGSSS has now awarded for the 2021/22 round of studentship awards. As such, this opportunity is only open to home students.

Residential Criteria

To be classed as a home student, applicants must meet the following criteria:

  • Be a UK national (meeting residency requirements), or
  • Have settled status, or
  • Have pre-settled status (meeting residency requirements), or
  • Have indefinite leave to remain or enter.

If you do not meet the criteria above, you will be classed as an international student and will not be eligible to apply. To establish if you would be classed as a home student, please see pages 4 and 5 of the UKRI eligibility guidance here.

Award details

The scholarship is available as a +3.5 (3 year PhD and placement) or a 1+3.5 (Masters year, 3 year PhD, and a placement) studentship depending on prior research training. This will be assessed as part of the recruitment process, however you can access guidance here to help you decide on which to apply for. The programme will commence in October 2026. The full ESRC studentship package includes, as advised by ESRC:

  • An annual maintenance grant (stipend)
  • Fees at the standard institutional home rate
  • Students can also draw on a pooled Research Training Support Grant (RTSG)

Other information

Essential: Appropriate social science degree; strong research and writing skills; basic competence in Python and familiarity with quantitative/computational approaches (training in NLP/text analysis will be provided); interest in national security/defence policy and corruption/governance; willingness to use mixed methods including qualitative case studies and (where feasible) interviews; strong professional and ethical judgement; ability to work collaboratively with an external partner and to manage time to meet funded submission requirements.

Desirable: Any prior experience with Python data analysis libraries, text-as-data methods, policy/NGO/government research, process tracing and interviews, or relevant area expertise.

The successful candidate will be based at the University of Edinburgh and will use Python to conduct reproducible computational analysis of the NSDDD corpus, before integrating these findings with comparative case studies and process tracing. Prior specialist NLP experience is not required: we welcome applicants with basic Python skills and familiarity with quantitative/computational approaches, and we will provide training in text-as-data methods, research design, and qualitative interviewing/process tracing. The studentship is delivered in collaboration with Transparency International Defence & Security (TI-DS) and will include regular knowledge exchange and a 3‑month placement (distinct from the PhD research) supporting applied defence integrity assessment and policy engagement.

How to apply

  1. Applicants must register on SGSSS Apply, completing their Equal Opportunities data.
  2. Applicants must apply via SGSSS Apply, uploading the following documentation:

    • Application Questions (answered within SGSSS Apply, no upload needed)
    • Academic transcripts
    • Academic prizes
    • Referee information
    • CV
    • Other information (if required by the advert)
We strongly encourage applicants review the applicant guidance document for more on the process. 

Please Note:

  • This is not an application to the relevant University, this is an application for SGSSS (ESRC) funding.
  • Students do not need a Masters/PhD offer from the relevant University before they can apply for funding, i.e. this studentship.
  • If successful in obtaining the SGSSS (ESRC) studentship, students can only start the funded studentship once they have an unconditional Masters/PhD degree offer from the relevant University. It is your responsibility to find out the University’s application process, including when you need to secure your offer, as SGSSS plays no role in this process.

This studentship opportunity will open for applications on 9th June.

Apply now via SGSSS Apply

Selection process

Applications will be ranked by an internal institutional selection panel, and you will be notified if you have been shortlisted for interview on or around 2nd April. Interviews will take place on or around 23rd April.

This studentship award is subject to the successful candidate securing admission to a PhD programme within the University of Edinburgh. The successful candidate will be invited to apply for admission to the relevant PhD programme.

Contact details

Name
Team SGSSS
Email
SGSSS Team (for questions on the application portal, only), for any questions on the project, email the supervisory team (see details in abstract above)

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