Smart peacekeeping: Deploying Canadian women for a better peace?
Bibliographic record
Abstract
Canada announced its renewed commitment to United Nations peacekeeping with a special mission to increase the representation of women through the Elsie Initiative. That announcement marks a crucial time to examine peacekeeping as a gendered project that requires reflection on power and inequality between states and peacekeepers through an intersectional analysis that pays attention to gender and race. The major justification for increasing the number of women in peacekeeping operations has remained instrumental: deploying more women will lead to kinder, gentler, less abusive, and more efficient missions. However, there is little empirical evidence to support these claims. This paper looks at Canadian peacekeeping and arguments for women’s increased representation in peacekeeping operations for improved operational effectiveness as a “smart” peacekeeping strategy. It looks at the contradictions and controversies in Canadian peacekeeping and gender and smart peacekeeping that includes the Women, Peace, and Security agenda in general and within Canada, operational effectiveness claims, militarized masculinity, and militarized femininity. Without qualitative empirical data from Canadian women peacekeepers themselves, smart peacekeeping claims, which “add women and stir,” are largely anecdotal and do not adequately facilitate meaningful change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".