The 3M Affair in Canada-China Relations, 10 December 2018 – 24 September 2021, and Why for Canada it May not be Over Yet
Bibliographic record
Abstract
Although I am not Chinese, China has long been an enormous part of my everyday life.I have been engaged with China and Chinese affairs since July 1980, first as a teenager doing voluntary service in Taiwan, then as an undergraduate student, then as a graduate student, and finally as a university professor of Chinese history and full-time professional China-watcher.My wife of thirty-seven years is from Taiwan and strongly self-identifies as Chinese.(Her parents fled to Taiwan in 1949 when the mainland fell to the communists, and when they became my parents-in-law in 1984 I watched in fascination how until their dying day they loved China as intensely as they detested Chinese communism.)I have thought of China every day for over forty years now, and during this time I have been watching China go through growing pains and undergoing searing and exhilarating transitions and transformations.But over these past few years, culminating with the 3M Affair (involving Michael Spavor, Michael Kovrig, and Meng Wanzhou), it has been painful and disconcerting for me to watch China descend into solipsistic fury, floundering and flailing about and insisting that almost all foreign countries are wrong and only China is right.This starkly Manichaean
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".