Education and Global Cultural Dialogue: Analyses of the Chinese Knowledge Diaspora at a Major Canadian University
Bibliographic record
Abstract
In an era where the knowledge economy is increasingly global in form, China is competing vigorously to strengthen its innovation system, of which its universities are a key element. Among China’s strategic advantages is the huge resource represented by its own highly skilled diaspora. Recognizing the potential of this resource, and in an era of skill shortages in key arenas, countries of migration, such as Canada, have targeted their migration schemes at highly skilled individuals, many of whom are mainland Chinese (Hugo 2006). A large number of mainland Chinese intellectuals work at universities abroad, often after having obtained their PhDs abroad. As knowledge carriers and producers, they are valuable human capital and are a target of national migration and innovation policies (Kuptsch and Pang 2006). Those working in Canadian universities become important assets to both Canada and China. However, there has been little empirical research on them, especially in local contexts and in relation to broader axes of spatial relations in state and society (Cartier 2003). Based on a case study of Westcoast University (a pseudonym), this chapter examines the potential to deploy China’s large and highly skilled diaspora in the service of Chinese and Canadian scientific and technological development. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".