Bibliographic record
Abstract
United Nations peacekeeping continues to be tied to Canadian identity, albeit in complex and contradictory ways. This research study was motivated by Canada's Elsie Initiative and renewed rhetorical commitments to feminist foreign policy under former prime minister Justin Trudeau. It draws on in-depth interviews with Canadian servicewomen and veterans deployed on UN peacekeeping operations from the 1990s to the early 2020s. Its aim is to understand how peacekeeping continues to be tied to Canadian identity, despite Canada's decline in contributions, and how Canadian women peacekeepers make sense of their roles on missions, including their views on whether their participation improves operational effectiveness. Interview participants described peacekeeping as both meaningful and fraught. Many resisted narratives that position women as solutions to complex mission challenges, noting the burdens of visibility and stereotyping, among others. Contributing to literature on Canadian peacekeeping and its gendered underpinnings, the findings can inform future policy directions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".