Socio-cultural dynamics in gender and military contexts: Seeking and understanding change
Bibliographic record
Abstract
LAY SUMMARY Today, changing the culture of the Canadian Armed Forces (CAF) is a high priority so that all members feel respected and included and do not experience discrimination, harassment, or any form of sexual misconduct. This article looks back at the CAF experience with gender integration to see what it tells us about what should be done today. Over 20 years ago, many believed the job was done, that the CAF had fully integrated women and welcomed all members, regardless of who they were. Women have served in the Canadian military for several decades; they make important contributions, and there are no formal limitations on how they contribute and what they can achieve. Although policies and practices have changed, too often, some women and men continue to experience discrimination, harassment, and sexual assault. Based on past experience, this article suggests that thinking about different ways of understanding culture in the CAF is important in paving the way for a more inclusive experience for all members.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".