Knowledge Diplomacy in the De-Risking Context: What Does “De-Risking” Mean for EU-China Higher Education Collaboration?
Bibliographic record
Abstract
The de-risking strategy rolled out by the European Union (EU) in 2023 was a major historical event for EU-China relations in higher education. In this study, 40 international education professionals from both China and the EU were interviewed to glean an understanding of what potential impact the policy may have on EU-China relations in higher education. Using knowledge diplomacy as a theoretical lens, this study aims to examine whether the higher education sector is able to balance national security and knowledge diplomacy in their international activities in the “de-risking” context. Despite some important concerns perceived, close to half of international educators from both sides are optimistic about the higher education sector's ability to transcend geopolitical tensions and continue collaboration. The confidence in the future prospect shows that de-risking presents challenges to collaborations, but the challenges do not necessarily eliminate the possibility of knowledge diplomacy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".