Tipping the scales of conflict: defence policy decision-making in the Canadian federal cabinet
Bibliographic record
Abstract
This dissertation analyzes how the Government of Canada makes decisions to deploy its forces to international conflicts through an examination of the 1996 Zaire peacekeeping mission, the 1999 Kosovo Air War, the 200 I and 2003 deployments to Afghanistan, and the 2003 Iraq War. Although studies of defence policy decisions are common in the United States (US), research on how the Government of Canada decides to go to war has received very little attention in academic literature. In an effort to help address this gap, this study engages two questions: how does the Canadian government decide to go to war? In addition, which influential factor(s) best explain Canadian defence policy decisions towards going to war? Through an in depth analysis of the decision-making process in each case, the dissertation argues that interdepartmental politics, the US, and international organizations, particularly North Atlantic Treaty Organization, play an important role in the decisions that Canada makes to deploy its forces.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".