Cross-Cultural Experiences of Canadian Science Educators Visiting the Sister Schools in Chongqing, China: A Cross-Cultural Experience
Bibliographic record
Abstract
The benefit of cross-cultural learning is two-fold: first is recognizing the practices and foundations that build education in another country. The second is reflecting on the educational practices within one’s home country. Cross-cultural learning also allows one to put the practice of education into perspective by having another side to which they can compare their experiences. This research is a narrative inquiry case study of the Canadian perspective and experience of two in-service science teachers who visited sister schools in China. This case study explores Canadian teachers’ perceptions of teaching science, what inquiry-based teaching looks like, what equipment aids in the process, and what experiences and teaching methodologies can be shared between Canadian schools and the Sister Schools in Chongqing, China. This research was funded by the Social Sciences and Humanities Research Council (SSHRC) Partnership Grant.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".