Canada and the United States: A Divergence in Attitudes Towards Defence and NATO
Bibliographic record
Abstract
The stark divergence in Canadian and American approaches to defence spending and NATO contributions highlights fundamental differences in how these allies conceptualize security. While the United States prioritizes hard power—allocating 3.4% of GDP to military expenditure and emphasizing coercive capabilities—Canada adopts a soft power strategy, focusing on diplomatic engagement, peacekeeping, and leadership in NATO missions despite spending only 1.3% of GDP on defence. This contrast has fueled tensions, particularly under the Trump administration, which labelled Canada a "free-rider" for failing to meet NATO’s 2% spending target. Yet Canada counters that its qualitative contributions—such as leading NATO battlegroups in Latvia and training missions in Iraq—demonstrate commitment through participation rather than pure financial metrics. The debate underscores deeper ideological divides: The U.S. views military and economic dominance as central to global security, whereas Canada champions multilateralism and institutional engagement. While these differing approaches have strained bilateral relations, NATO’s structure accommodates both, as seen in Article 2’s emphasis on democratic values alongside collective defence. Moving forward, the challenge lies in balancing hard power expectations with soft power’s diplomatic merits, especially as the Biden era reshapes transatlantic priorities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".