Becoming Globally Competent Through Inter-School Reciprocal Learning Partnerships: An Inquiry Into Canadian and Chinese Teachers’ Narratives
Bibliographic record
Abstract
This study explores how a Canada–China Sister School Network provides school-based professional learning opportunities for in-service teachers to grow their knowledge and capacity to educate for global competence and citizenship (GCC). In particular, it presents the story of a Canadian teacher and a Chinese teacher who had found ways of educating for GCC through carrying out intercultural and international reciprocal learning in a researcher-supported inter-school reciprocal learning partnership. By inquiring into the Canadian and Chinese teachers’ growth narratives, this study highlights four lessons teachers, educators/researchers, and policy makers may learn from the two teachers. It concludes by highlighting the potential of a relationship-oriented, open-ended, and non-hierarchical international school network in supporting teachers to become more globally competent, foregrounding reciprocal learning and collaboration among school practitioners and researchers.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.045 | 0.029 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| 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".