Prospects for Canada's Participation in Multilateral Alliances in the Indo-Pacific Region
Bibliographic record
Abstract
Over the past few years, Canada has sought to expand its military-political and economic ties with the countries of the Indo-Pacific region (IPR) and to play a more active role there. As a means of achieving this goal, Canadian policymakers and experts are considering their country's participation in the various types of alliances and partnerships that have emerged in the IPR in recent years. Canada's greatest focus is on the AUKUS military-technology partnership, formed in 2021 by Australia, the United Kingdom, and the United States. Canada wants to participate in the second pillar of this partnership, the development of new military technologies. Canada is much less interested in participating in the Quadrilateral Security Dialogue (Quad). Canada's interest in participating in the Quad has especially waned since the creation of the AUKUS. Canada also wants to participate in the Indo-Pacific Economic Framework for Prosperity (IPEF), but in this case the initiative comes from Canadian big business rather than the government of J. Trudeau. Canada's participation in the Five Eyes intelligence alliance holds some promise. The possible admission of Japan to this alliance will transform it from an Anglo-Saxon to a Pacific alliance. With the beginning of D. Trump's second presidential term, Canada's participation in multilateral structures in the IPR has been seriously hampered. On the one hand, with the sharp deterioration of relations between the USA and Canada, membership in U.S.-centered regional alliances has lost its former appeal for Canada. On the other hand, the Trump administration would also not consider Canada a desirable partner in such regional alliances. Admitting Canada into such structures contradicts Trump's own logic that Canada should become the 51st American state. Another factor that makes it difficult for Canada to participate in multilateral structures in the IPR is the crisis in its relations with India, which is a member of many such structures and wields enormous influence in the region.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.052 | 0.007 |
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".