Bibliographic record
Abstract
Abstract The chapter analyses the policies of Canada and Norway in NATO. While the two countries have distinct regional security priorities, they place NATO, as well as their bilateral relationship with the United States, at the core of their foreign and defence policies. Domestic political support for NATO remains strong in both countries. While they both share a strong interest in promoting greater Alliance cohesion and are committed to tackling ‘emerging security threats’, they have distinct views on threat assessment and the relative expansion of NATO’s military tasks above and beyond collective defence and deterrence. In the case of Norway, the direction of Russian defence and security policy under Putin, rapid changes in weapons technologies, the weakening of existing arms control regimes, and the uncertain effects of environmental pressures in the Arctic and the High North have all combined to bring its NATO priorities back to its historical core: securing reinforcements to the Northern flank in the event of crisis and ensuring that key allies maintain an interest in NATO’s Northern periphery. For its part, Canada has made NATO a priority by contributing to its operations and activities, while decreasing its involvement in UN operations. NATO represents a useful diplomatic forum for a country which is dependent on its bilateral defence cooperation with the United States. Ultimately, what sets Canada apart from Norway is a threat perception tempered by the security benefits that three oceans and a powerful neighbour offer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".