Bibliographic record
Abstract
Stopping wars, keeping them from restarting, and observing the implementation of cease-fires—various shades of “peacekeeping”—have always been dangerous work. It has become particularly so over the past decade as peacekeepers increasingly find themselves in high intensity, near-war situations. No longer are belligerents constrained by world power blocs as they were before the end of the Cold War.\nThis is reflected in the list of the 107 Canadian soldiers who have been killed worldwide since 1950 while serving as peacekeepers, including 22 killed since 1992. The Peacekeeping Memorial in Ottawa reflects Canada’s tribute to this sacrifice and the nation’s pride in its soldiers’ contribution to world peace. Too often, however, Canadian peacekeepers feel the courage they display and the sacrifice they make are ignored by the media and forgotten by an uncaring Canadian public. Police officers who have been killed in the line of duty draw extensive media coverage and large, elaborate funerals, but fallen soldiers appear to elicit no such attention. One response of peacekeepers in the field to this perceived indifference has been to create their own memorials to fallen comrades. Over the years, I have visited a number of these locally made memorials, both in the former Republic of Yugoslavia and in the Middle East. This article tells their story.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.080 | 0.009 |
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".