Secondary analysis of qualitative data in defence studies: opportunities and challenges
Bibliographic record
Abstract
This chapter will focus on a very rare topic in the academic literature: the secondary use of qualitative data collected by another researcher. The practice of secondary analysis has developed in political science and sociology over the last decade. Yet it raised specific issues in defence studies, as the formerly collected data can be put under the shade of secrecy or confidentiality. Thus is it possible to conduct a secondary analysis of qualitative data collected on defence questions (as interview data for instance)? Under what conditions can this be done? What are the limits encountered by the researcher when leading secondary analysis of qualitative data when it comes to defence topics? The chapter will first explain what is secondary analysis of qualitative data in defence studies, and what kind of methodological and ethical questions this method raises. It will secondly analyse the conditions needed in order to proceed to secondary analysis in defence studies. We will illustrate our argumentation with a case study regarding the secondary analysis of qualitative data collected on French-German military cooperation and defence policies and underline the potentiality and limits of this specific methods for defence studies, as Laurent Borzillo led a secondary analysis of qualitative data collected by Delphine Deschaux-Dutard on French and German defence policies in the frame of European defence policy since the end of the Cold War.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.583 | 0.641 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.016 | 0.026 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.031 | 0.026 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".