Missing the Point: A Critical Reflection on Operation HONOUR and Reactions to Military Sexual Misconduct by Veterans of the Canadian Armed Forces
Bibliographic record
Abstract
While there has undoubtedly been progress made in regards to the inclusion of women and LGBTQ+ individuals as full members of the Canadian Armed Forces (CAF), it is questionable as to whether the organizational culture has shifted since these efforts were initiated almost thirty years ago. This article argues that resistance to culture change is based in sexist beliefs and attitudes, which are most noticeable in discussions related to Operation HONOUR, the CAF initiative meant to purposefully change military culture in an effort to eliminate sexual misconduct. The article critically reflects on how the CAF has presented results from surveys aimed at examining the beliefs and perceptions of current serving members in regards to sexual misconduct in the military. It argues that the CAF is missing key points of analysis, particularly in failing to identify and analyse the beliefs, attitudes, and behaviours that make up a problematic and misogynistic military culture. To address this, the article presents a taxonomy of sexism to help understand the attitudes of soldiers and veterans. Based on this taxonomy and informed by long-term and ongoing ethnographic research, the article then outlines a spectrum of behavioural archetypes, namely: (1) allyship to victims, (2) willful blindness to the prevalence of and harms caused by military sexual misconduct, and (3) a negative and misogynistic response tied to what has been termed as toxic masculinity. Understanding these behaviours and their embeddedness in veterans’ self-perceptions and the military’s culture is key to achieving CAF culture change in the context of systemic sexual misconduct.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.056 | 0.032 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".