How emerging trends in historiography expose the Canadian Army’s past discriminatory practices and provide hope for future change
Bibliographic record
Abstract
This “lessons learned” article examines how emerging trends over time in the historiography of the Canadian Army have challenged and continue to challenge the white Anglophone masculine heterosexual culture which is especially associated with its combat units. This study began as an examination of the intersection between the historiography and the current priorities for sufficient female participation in the Canadian Armed Forces (CAF) which are intended to improve past abusive patriarchal practices and create effective and safe international interventions. Gender and sexual abuses were the initial foci, but the historiography revealed the interconnectedness of widespread discriminations against all “others”—defined here as anyone with a different gender, sexuality, race, language, religion, or culture. The article opens with a brief summary of evolving feminist ideas about security forces in general. It then delves into the historiographical trends which have demonstrated how systemic discriminations have privileged white Anglo men in combat roles while underplaying their contributions and the contributions of “others” in support roles in the Canadian Army over time. The key lesson learned from this work is that gender balance alone is not enough to address the profound cultural issues which plague the Canadian Army.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".