Returnees as Transnational Diaspora: Exploring Transnational Academic Connectivity from the Experiences of Internationally Educated Chinese Academic Returnees
Bibliographic record
Abstract
Transnational migration brings to the fore the various social and professional connections migrants maintain with their home and sojourn countries. This paper explores how such connections gained through doctoral studies abroad affect Chinese returnee academics’ work and learning in the transnational setting, as well as their senses of belonging. This study employs the methodology of a qualitative case study of 12 internationally educated Chinese academics from the social sciences and humanities within three higher education institutions in Beijing, China. Through the theoretical lens of diaspora, it identifies that the academic and social connections returnee academics gained and maintained with their supervisors and former colleagues in their host countries of doctoral studies have become significant ties that orient their intention and predilection for transnational research and academic collaboration. Returnee academics have also indicated to have actively engaged overseas academics of Chinese origin in research collaboration. The study suggests that Chinese academic returnees seem to have formed a virtual transnational diaspora, and have contributed to strengthening the inter-dependence of academics across borders through academic and research collaboration.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".