The Chinese Knowledge Diaspora and Diaspora Knowledge Network: Australia and Canada compared
Bibliographic record
Abstract
In the context of globalization and the knowledge economy, brains, increasingly mobile, have assumed unprecedented importance, and even more so in the coming decades when the academic profession is ageing. Developed nations like Australia and Canada compete to attract and retain the best and brightest. A related development, advancement in information and communications technology, enables the establishment of powerful cross-boundary research networks. The study builds on previous research in order to understand the Chinese knowledge diaspora in Australian and Canadian universities, and trace their transnational intellectual networks to colleagues in mainland, and other parts of the Chinese intellectual diaspora. A qualitative, grounded theory approach was used for the study. Semi-structured in-depth interviews were conducted for data collection. The data gathering techniques yielded a rich volume of detailed descriptions that were categorized and thematically analyzed. The study investigates the role of the Chinese knowledge diaspora, and the dynamics of the diaspora network with special reference to the factors that both sustain and limit such transnational knowledge networks. The strong sense of cultural/ethnic identity and motivation for closer academic ties were reiterated, as were commonly-expressed sentiments that doing science in the West was a primary source of satisfaction. Substantial accounts of scientific communication and transnational collaboration were highly illustrative. While some had one or two kinds of interaction, most had multiple types of collaboration with China. The influencing factors at personal, institutional, and system level were well documented and categorized. Among the most prominent were the differences of research culture across the two systems. Limitations of this study include small sample size and distribution. Recommendations for future study include increasing the sample size, recruiting indigenous scholars and administrative staff, examining the Chinese knowledge diaspora from both research-intensive and less research-intensive universities in the United States, and investigating further how gender affects both academic being and knowledge networks with the mainland academia of China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".