Canada and China a fifty-year journey
Bibliographic record
Abstract
"Presenting a thorough record of Canada's diplomatic ties with China, Canada and China recounts ten stories regarding China policy decisions made by the Canadian government. These decisions describe key bilateral moves, beginning with Pierre Trudeau and recognition in 1970, and ending fifty years later with his son Justin as Prime Minister. Rooted in archival research, extensive interviews, and B. Michael Frolic's lived experience as a policy observer, the book contributes to our understanding of how the Canada-China relationship has developed over time and how best to position Canada in future relationships with China. The ten decisions discussed in the book are the result of two decades of research and behind the scenes discussion with Prime Ministers, Ministers, Ambassadors, and China specialists, revealing the challenges, successes, and limitations of our engagement with China. While present-day relations with China are complicated, the book deliberately seeks to provide a balanced perspective by showing both the positive and the more challenging aspects of relations with China. Canada and China concludes that we should maintain the ties we have developed over fifty years and recommends ways to manage our future relations."--
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.042 | 0.016 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".