Preparing for future security challenges with practitioner research
Bibliographic record
Abstract
Mid-sized countries face a changing security environment, and cannot be certain that the knowledge and practices of the past will serve the future. The officers, professors, and researchers in defence universities are the custodians of military sciences that must adapt to these changing situations. Practitioner research should be modelled and encouraged in defence universities as a vehicle for advancing military sciences to meet new challenges. Previous practitioner research in higher and adult education has highlighted the need for experiential learning in other professions. The authors report on practitioner research by professors at pre-commission military academies to improve cadets’ understanding of peace and conflict. Military and police education is often experience-based, but there are few reports of practitioner research on its effectiveness, nor of combining peace and conflict education with out-of-classroom experiences. Legitimation Code Theory provides tools for understanding different teaching approaches. Comparing four cases of practitioner research on experiential learning the authors present models for practitioner research on teaching peace and conflict through out-of-classroom experiences, and conclude with means of evaluating learning experiences by pre-commission cadets, drawing on legitimation code theory. This is increasingly important for military academies striving to meet academic standards, but also to preserve professional values and young officer motivation to confront new challenges.
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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 | 0.144 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.032 | 0.032 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.020 | 0.031 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".