Lessons from the Frontline: The Burgeoning Security and Economic Race in the Indo-Pacific
Bibliographic record
Abstract
Democracies in the Indo-Pacific are facing an increasingly competitive and securitized environment, as China turns more assertive and the US-China rivalry escalates. Disruptions include uncertainty about the global economic order, technological decoupling, an arms race amidst intense hot spots, an economic security spiral, and larger systemic risks around climate change, AI governance, etc. Countries such as Japan and Korea are actively responding to these risks through a strategic approach to economic security and increased defense spending, active alliance development, and parallel continued investment in global institutions and dialogue. Canada must be jolted out of its-business-as-usual approach and take a cue from its closest partners in Asia. This includes investing rapidly in its security (2% of defense over GDP) and the creation of an economic security council under the Prime Minister. Canada should also join its key partners in effectively investing in key global institutions in times of crisis.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".