Peackeeping, Peace, Memory: Reflections on the Peacekeeping Monument in Ottawa
Bibliographic record
Abstract
Since 1948, under the auspices of the United Nations (UN), Canada has contributed over 80,000 men and women from all branches of the armed forces to global peacekeeping. During the 1950s and 1960s, Canada was, in fact, the greatest contributor of ’Blue Helmet’ soldiers to UN peacekeeping endeavours and became the undisputed leader in global peacekeeping. Although peacekeeping was never the sole preoccupation of Canada’s foreign policy, Canadian politicians liked to be seen as projecting an image as ’helpful fixers,’ acting as a voice of moderation between the extremes of the two superpowers during the Cold War. It was a Canadian statesman, Lester B. Pearson, who first used the UN Charter to create the idea of an international peacekeeping force—a concept that earned him a Nobel Peace Prize in 1957.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.103 | 0.040 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 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".