Research on China’s Higher Education Delivery Offshore in the Post-Pandemic Era
Bibliographic record
Abstract
Offshore education, as an effective way to enhance the international flow of education service and an efficient platform for the higher education interconnection and knowledge sharing all around the world, is a significant part of the “opening-up” strategy of education made by China’s Ministry of Education. However, Chinese universities and colleges which plan to run school offshore are currently facing challenges such as the great changes of global governance, the spread of Covid-19 pandemic, the changes in domestic laws and policies and the greater participation of vocational colleges. This should be attached more importance by researchers and policy makers in order to find an innovative and appropriate mode of international cooperation and exchange in the post-pandemic era. Based on the analysis of definitions of higher education delivery offshore and the theoretical and practical causes of the challenges, Chinese universities should clarify the orientation and direction, attach importance to the development of vocational schools offshore, promote overseas schools to become offshore platform for innovation and international exchange, and enhance the international competitiveness of oversea schools by full advantages of government, universities, enterprises and industry organizations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".