Bibliographic record
Abstract
Over recent years, Ottawa had sent signals of a desire to re-engage the Asia-Pacific via stronger ties with Southeast Asia and ASEAN, yet continued to struggle in devising a clear path forward that would help distinguish itself from other “dialogue partners.” This article reviews Canada’ mixed track record in the region, with a focus on its relations with ASEAN. It discusses how this regional organization fits into Ottawa's long-awaited Indo-Pacific Strategy (IPS) released in November 2022. It argues that Canada cannot succeed in its ambition to be recognized as an “Indo-Pacific” nation without developing, advancing, and implementing a coherent and consistent approach to Southeast Asia, and ASEAN in particular. Further, these efforts should be anchored in a more convincing narrative that ties Canada's contributions to the region together, aligns with regional perspectives, and are reflected in targeted but nonetheless substantial contributions at the intersection of security and development. This paper reviews key moments that have come to define the evolution of Canada-ASEAN relations over the past four decades or so, provides a balance sheet of Canada's successes and shortcomings, and discusses potential next steps and ongoing challenges as it turns to implementing its IPS.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".