Military culture and change: How a NATO workshop emphasizes the imperative of critical paradigms
Bibliographic record
Abstract
Introduction: The study of military culture surged in the 1990s as military organizations faced human rights challenges in their home nations and while deployed abroad. In recent years, amid heightened public pressure to stem gender-based violence and other forms of systemic misconduct, military culture change has taken on increasing importance. The discussion begins with an overview of the literature that has shaped understandings of military culture and culture change. Methods: The analysis presented in this article engages with the academic literature to examine key insights emerging from a North Atlantic Treaty Organization (NATO) research workshop. Results: The analysis suggests three priority research themes to enhance knowledge about military culture change: the critical relevance of root causes and intersectionality to understanding cultural challenges; interpreting resistance to change as connected to operational effectiveness, military identity, and the concept of buy-in; and reframing research to include critical, transformative, and trauma-informed paradigms. Discussion: Engagement with critical paradigms to address individual, organizational, and structural factors shaping the dynamics of military culture is crucial for transformative culture change. As military organizations move forward to better the experiences of their personnel, the analysis underscores the need to strengthen critical knowledge and capability across policy and research communities in partnership with those who will be impacted by the research and the knowledge that these processes create and embed within military culture.
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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 | 0.097 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.033 | 0.039 |
| Scholarly communication | 0.034 | 0.028 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.013 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".