“Proud, brave, and tough”: women in the Canadian combat arms
Bibliographic record
Abstract
, emphasizes the importance of leveraging Canada's diversity to strengthen the Canadian Armed Forces. Currently, women in the Canadian military are underrepresented across most elements and occupations, especially in the combat arms occupations, including among officers and non-commissioned personnel in combat units such as infantry, armored corps, artillery, and combat engineering. Research suggests that the benefits associated with the inclusion of women in combat arms occupations include an increase in collective intelligence, operational effectiveness, task cohesion, and diversity. This article explores the gender gap in the Canadian combat arms by examining the findings from two recent qualitative research studies on the perceptions of women in the Regular Force and Primary Reserve. The authors analyze female military personnel's perceptions of women serving in the combat arms, and the ways to increase their inclusion in the military. The key findings reveal the following themes on women's perceptions of servicewomen in the combat arms: great job for those who want it; challenging environment (e.g., working within a masculinized culture, necessary toughness, tokenism and the "pink list," being treated differently, and family loyalty); unique challenges faced by women in combat roles; combat takes a toll on women's mental and physical health; and benefits of women's participation in multinational operations. The discussion highlights the need to increase diversity, equity, and inclusion, promote a culture change that fosters greater inclusion of women in the combat arms, and increase operational effectiveness through training and policies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".