Military Security and Research Ethics: Using Principles of Research Ethics to Navigate Military Security Dilemmas
Bibliographic record
Abstract
Collecting data and working with classified information in restricted military settings can present significant research challenges. Academic ideals of transparency and openness clash with the military’s need for secrecy and closedness. This article engages with existing literature on security requirements and research ethics in discussing practical challenges researchers face in military research. Even though military security requirements and principles of research ethics are often perceived as opposites, they also share characteristics: both realms are context-driven, non-objective, and require professional judgment to assess. Through a four-part analysis corresponding to different steps in a research process, the authors develop a practice-oriented guide for researchers accessing and working with classified information in discussing mundane examples of how “insiders” with “privileged access” navigate between ethical research principles and security issues. The article also incites a broader debate on research governance, (self-)imposed restraints and the conditions for critical inquiry in the military domain.
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 | 0.307 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.152 |
| Scholarly communication | 0.030 | 0.028 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".