Canada’s Approach to Climate Cooperation in the Indo-Pacific: Analysis and Suggestions for Canada’s Engagement with ASEAN Countries under the Indo-Pacific Strategy
Bibliographic record
Abstract
Drawing evidence from governmental datasets, policy reports, and other sources, this paper, focusing on the clean energy transition as the key aspect, examines Canada’s current approach to climate cooperation with ASEAN countries to “build a sustainable and green future,” echoing one of the strategic goals in Canada’s newly released Indo-Pacific Strategy (IPS). In particular, this paper argues that the current policy and financial assistance from Canada to ASEAN countries is unequal and insufficient. Moreover, there is a lack of continuity and measurable objectives within this cooperative framework, which primarily operates within a multilateral context. Given these characteristics, we propose that Canada's cooperation with ASEAN nations in addressing climate change should integrate the imperative for a clean energy transition. This approach has the potential to create numerous opportunities for engaging with ASEAN countries, while also aligning with the objectives of the IPS. Specifically, to enhance the effectiveness of IPS in this context, the Canadian government should emphasize experience sharing, fortify its business collaboration with ASEAN countries, and participate more actively in ASEAN-led initiatives.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".