Post-crisis response by Chinese stakeholders to Canadian international education programs: a case study with community-based survey evidence
Bibliographic record
Abstract
Purpose This article examines education diplomacy as a specific application of public diplomacy in stabilizing Canada–China relations, which have worsened over the last few years. It conducts a case study analysing post-crisis responses of Chinese stakeholders in Canadian university international programs. The survey results provide policymaking insight for restoring post-crisis global learning activities. Design/methodology/approach It applies conceptual analysis, comparative methods and historical reflection to design a community-based survey. It treated Chinese university students and scholars as stakeholders of education diplomacy. Utilizing an established network of Chinese intuitional partners by the host institute, this case study analyses questionnaires on the online survey platform Qualtrics. Findings The survey indicates concerns about diplomatic tension by Chinese stakeholders in Canadian university international programs. However, their responses are still favourable for resuming global learning activities with more flexibility, mobility and personal safety facilitation. Originality/value The paper assesses the post-crisis response of Chinese stakeholders concerning the Canada–China education collaboration while interpreting Education Diplomacy as a specific form of Public Diplomacy for normalizing China–Canada relations still subject to growing bilateral tension.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".