Canada and the Changing Security Architecture of the Indo-Pacific Region
Bibliographic record
Abstract
The growing confrontation between the U.S. and China is drawing an increasing number of countries into this process. For Canada, which is more closely tied to the U.S. than any other country in the world, choosing a side in this confrontation was an obvious decision. Over the past two years, Canada has taken active steps to support the U.S. strategy to contain China in the Indo-Pacific region. First, in November 2022 the Trudeau government published Canada's Indo-Pacific strategy for the first time and allocated funds for its implementation. As part of this strategy, Trudeau’s government began annually deploying an additional (third) frigate to the region, increased the participation of the Canadian Armed Forces in military exercises alongside American and Japanese troops, and is developing military-technical cooperation with South Korea. Like the U.S., Canada also supports the Philippines in its territorial dispute with China in the South China Sea. Mirroring the actions of the U.S. and its closest allies in the region, Canada signed several defense and security agreements with the Philippines between 2023 and 2024. However, the U.S. does not consider Canada a valuable partner in the region because of its limited military strength and excludes Canada from new multilateral structures in the region. Canada's inability to fit into the new Indo-Pacific security architecture may encourage it to integrate into this regional security system not through U.S. assistance, but by establishing closer military ties with the United States' primary ally in the region, Japan. This approach would allow Canada to become part of the new Indo-Pacific security architecture alongside Japan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".