Brain Drain, Brain Gain and Brain Circulation: Emerging Trends and Patterns of Chinese Transnational Talent Mobility
Bibliographic record
Abstract
Abstract Four decades ago, in the 1980s, network analysis did not anticipate the growing importance of transnational and diasporic communities “embedded” in more than one country. The integrating theoretical framework of transnational social mobilities is adopted for this article on Chinese talent mobility in the diaspora with a focus on the mobilities between China and countries in the OECD. By examining macro migration data within the OECD, we analyze emerging trends and patterns of the movement of highly skilled Chinese transnational talent. In light of China’s rising economic power and the concomitant growth of a Chinese transnational diaspora, we trace the trajectory of China’s brain drain, brain gain and brain circulation in OECD countries over the past forty years. Chinese talent mobility in the new economy consists of “transnational circuits” characterized by the circulation of goods, people, knowledge and information. This article theorizes the new modality of “circulation” in terms of transnational social mobilities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".