Working in a space of contradictions: military culture change work in Canada
Bibliographic record
Abstract
This article explores what we have learned about Critical Military Studies (CMS) from bringing a critical lens to culture change efforts within the Canadian military. Funded by the Department of National Defence (DND), from 2022 to 2025, we ran the Transforming Military Cultures (TMC) Network, comprised of Canadian and international academics, defence scientists, military members, and veterans with an interest in advancing military culture change. Critiquing and challenging the organization we were funded by was often contradictory and always complex work. In this article, we reflect on the social, political, and institutional context of our engagement with DND/CAF. We describe the unique risks, tensions, and possibilities that arose, including the backlash and silencing we experienced when publishing our work in the Canadian Military Journal. We argue that CMS scholarship requires us to navigate the ongoing dynamic of the military’s institutional commitment and resistance to culture change alongside growing political polarization. Our work has reinforced the importance of CMS’s call to work within spaces of contradiction rather than avoiding the complexities of engaged scholarship. While we encountered limitations and pushback as CMS scholars engaged with the military, we argue that there still is value in working in this space of contradictions. We conclude by reflecting on what our experiences reveal about the possibilities and limitations of CMS in this particular moment in time and location within North America.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.105 | 0.050 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".