Women, Citizens and Soldiers: The Gender Integration of the Canadian Forces
Bibliographic record
Abstract
The purpose of this thesis was to do a gender-based policy analysis of the gender integration program of the Canadian Forces, looking specifically at women in the combat arms (Infantry, Armoured, Artillery and Combat Engineers). In researching this thesis, I studied primary policy documents extensively, as well as engaging in both participant and unobtrusive observation at Area Training Centre Meaford and Combat Training Centre Gagetown. The research revealed that, despite recent efforts to achieve gender neutrality by the leadership of the Anny and the Canadian Forces, what it takes to be perceived as a good soldier remains inextricably linked, with what it takes to be perceived as a good man. In other words, life in the combat am1S remains predicated on the male norm. It is my argument that it is exactly this attempt at gender neutrality that inhibits women's integration into the combat arms, by masking the continuance of male privilege through the perception of difference as equivalent with inferiority. This translates not only into unofficial attitudes of soldiers, but also into training standards, equipment and a social infrastructure that assumes that men are the norm. I, therefore, argue that only when women are recognized as different, without assuming physical or social inferiority, will the CF be able to successfully integrate women as equals into the combat arms.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.018 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".