Are Her Boots on the Ground? Women’s Deployment on NATO-led Operations
Bibliographic record
Abstract
While women’s representation in many western military forces is increasing, the composition of troop contributions to NATO-led operations is not following suit, despite policy commitments acknowledging the importance of diversity in operations. Are Her Boots on the Ground? Women’s Deployment on NATO-led Operations questions why this may be the case. Determining what factors influence women’s military deployments and participation is essential to meet NATO’s international commitments, and member states’ ongoing commitments to gender equality. Using a mixed-method approach comprised of interview data with NATO elites and Canadian and Danish force generators, and a survey of military members from Canada and Denmark, I assess common explanations for barriers to women’s participation in masculine occupations. I argue that hidden resistances that comprise military organizational cultures hinder women’s deployment on NATO-led operations. These resistances are taken for granted and considered unchanging elements of military service, and include gendered assumptions held by force generators, the force generation process itself, and a hands-off approach to committing to more women on operations by NATO, Canada, and Denmark. In short: it is not simply that there are not enough women to deploy. The dynamics at play are much more complex. Ultimately, with a blind eye turned to these processes and assumptions, women will continue to be left behind on NATO-led operations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".