Preparing for peace: Myths and realities of Canadian peacekeeping training
Bibliographic record
Abstract
During the Harper years (2006–2015), Canada significantly reduced the training, preparation, and deployment of military personnel for United Nations (UN) peacekeeping. Now, despite the Trudeau government’s pledge to lead an international peacekeeping training effort, Canada’s capabilities have increased only marginally. A survey of the curricula in the country’s training institutions shows that the military provides less than a quarter of the peacekeeping training activities that it provided in 2005. The primary cause of these reductions was the central focus on the North Atlantic Treaty Organization’s Afghanistan operation and several lingering myths about peacekeeping, common to many Western militaries. As the Trudeau government has committed to reengaging Canada in UN operations, these misperceptions must be addressed, and a renewed training and education initiative is necessary. This paper describes the challenges of modern peace operations, addresses the limiting myths surrounding peacekeeping training, and makes recommendations so that military personnel in Canada and other nations can once again be prepared for peace.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.056 | 0.037 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".