Reimagining China–US university relations: a global ‘ecosystem’ perspective
Bibliographic record
Abstract
This study is among the few that attempt to connect two popular topics, the rapid growth of Chinese higher education and the shifting China–US university relations. Now both the Chinese and US higher education are among the top systems in the world—in terms of their sizes and standards. While Chinese and American university ties have been among the most important higher education relations, they now move towards decoupling. Against this backdrop, this study situates the growth of Chinese higher education and China–US university relations in the discourse of a global higher education ecosystem, and explores what China–US university relations would mean to this global ecosystem, how they may evolve, as well as the implications for the global ecosystem. This study draws on Marginson's four heuristic narratives explaining science inquiries as a global space of activity and perception, and develops an analytical lens to apply to data collected from relevant databases and literature in order to reimagine China–US university relations in the various models. Finally, this study maintains that the universities in both countries share the obligation and responsibility to work together for sustaining and nourishing this global higher education ecosystem, particularly in an Anthropocene epoch.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".