Current and Future Challenges to Canada-US Defense Cooperation – North America, the Transatlantic, and Beyond
Bibliographic record
Abstract
Canada-US bilateral defense relations exhibit asymmetry and interdependence due to shared geography, different capabilities, and a combination of informal defense agreements (such as the North American Aerospace Defense Command, henceforth NORAD), alongside formal treaty commitments. The dynamic results in a status quo where Canada relies upon bilateral and multilateral institutions to manage the externalities of (geo) strategic challenges, compensate for its defense limitations, and contribute to collective defense goods. Unfortunately, combined continental and transatlantic myopia produced underdeveloped security relationships in the Indo-Pacific region, evidenced by the absence of solid defense diversified cooperation. Despite this, Canada has responded to Russian aggressive behavior by offering financial and military contributions to Ukraine, potentially leaving its territorial defense vulnerable. Public commitments to modernize continental aerospace warning capabilities have been made, yet Canada continues to face complex threats, i.e., (dis)information, cyberattacks, hybrid threats, and the forces of extremisms, populisms requiring continued awareness, mitigation, and adaptation, alongside partner collaboration in informalized institutions. This paper discusses several strategic geographic policy areas where increased defense cooperation efforts are essential to protect defense and security interests, including the Arctic and climate change and security. Taken together, after reviewing seven challenges that both countries face, Canada finds itself compelled to rely on a combination of multilateral, bilateral agreements and collaborative institutions simultaneously attempting to constrain as well as regulate US foreign and defense policies to mitigate rational strategic challenges and ensure Canada’s territorial defense and national security interests are met.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 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".