Balancing the dragon softly: formulating a pragmatic China policy for Canada
Bibliographic record
Abstract
China's rising power has raised many questions around the world as to how best to adjust to this historic event in our time.This debate is heating up particularly in the Asia-Pacific region where China's military power is growing.Canada's fellow democracies in the region have engaged in lively security policy debates about the rising Chinese power.Yet, Canada has not.Its policy discourse on China is largely limited to the question of how Canada should balance its concerns for human rights with its interests in trade when dealing with China.Consequently, Canada suffers from the lack of a systematic, realistic, and pragmatic security policy framework in formulating its strategy toward China, which would unify various activities pursued by Canada's security policy community while taking into account the strategy of the United States, Canada's key ally.This project attempts to fill this critical void in the policy literature.It advances a "softbalancing strategy" that focuses largely on non-military measures in coping with security threats emanating from China.More specifically, it advocates that Canada should (1) foster further cooperation with its "Five Eyes" partners (the United States, the United Kingdom, Australia, and New Zealand) in the field of intelligence and cyber security; (2) forge stronger ties with the Asian partners that share security interests vis-à-vis China; and (3) strengthen the existing policy regime to scrutinize inbound Chinese investment in Canada.While Canada should continue its engagement policy vis-à-vis China, it needs to be balanced with proper security measures in order to protect its security interests.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.012 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".