Bibliographic record
Abstract
A continuing debate in feminist scholarship on gender, security, and the military has been whether militaries can facilitate feminist progress and be forces for good. Feminists committed to working outside of militaries note that gender perspectives have often been used to advance the military’s goals of winning wars rather than commitments to feminist social transformation of military institutions and societies. However, influences from international normative frameworks on Women, Peace and Security; Canada’s feminist foreign policy; and an emphasis on diversity and inclusion within Canada’s Defence Policy have presented the Canadian Armed Forces with a solid platform from which it has begun to make change. The central tenets of this broad feminist platform have begun to permeate Canadian Professional Military Education (PME) through the collective efforts of educators, staff, and military students at Canada’s defence colleges. Drawing on a review of policy and programmes as well as a qualitative analysis of interviews with educators, staff, and military students, the article demonstrates that feminist transformational change by military members is possible by exploring its nascent reality. The article highlights the challenges and benefits of incorporating feminist perspectives in Canadian PME and demonstrates how and under what conditions military graduates with this education have begun to apply gender and cultural learning to make local feminist interventions both within and outside their institution. Ultimately, this research shows that collective efforts toward localized and incremental changes by military members are paving the way for meaningful feminist progress within the military.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.032 | 0.082 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".