Mutual constitution or convenient national interest? The security strategies of Canada and the United States since 1991
Bibliographic record
Abstract
The end of the Cold War marked the beginning of one of the most intensive periods of overseas operations for the Canadian forces, encompassing two decades of near continuous deployments in zones of conflict. The War in Iraq stands as an aberration amid a number of multinational operations in which Canadian forces have operated alongside their American counterparts since the end of the Cold War. In all other instances from Somalia to Libya and the many stops in between both Canadian and American forces have been actively engaged in the same multilateral operations and have seemingly taken a very similar response. In this paper we take a closer look at the factors that have led the American and Canadian governments to dispatch their forces to these conflicts and humanitarian crises. To do so, we examine the influence of international institutions and collective security agreements that bind Canada and the US together, such as the UN and NATO, as well as domestic debates on the relative merits of values and interests in accounting for these deployments. Our ultimate goal is to assess whether the respective commitments of the Canadian and American governments reflect a common approach to contemporary security issues and the consequences of their respective strategies for the future defence relations of the two nations.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.040 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".