Bibliographic record
Abstract
I n order to put my thoughts in context, I begin by outlining recent Canadian participation in the international sphere. I want to highlight that Canadian Forces operations are not limited to peacekeeping as is often misunderstood, not only on the international scene, but also sometimes at home. While Canada chose not to be involved in the 2003 Iraq operation, it has been a fully committed memberin terms both of the lives of its soldiers, sailors and ainnen, including women, as well as of treasure in the coalition and international efforts related to what the United States, our dose neighbor to the south, has termed the Global War on Terror or the GWOT, and what we call the Campaign Against Terrorism or the CAT.l I suppose this subtle use of different terminology is part of the reason this volume contains two other articles2 authored by represen tatives of nations that have participated in coalition operations with the United States. Together they illustrate the differing national approaches and understandings relating to participation in a common enterprise. Regardless of how the conflict is termed. countering AI Qaeda requires a multidisciplinary and multifaceted approach involving civilian and military intelligence agencies. policing. diplomacy and international engagement. as well as the
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".