Culture Shock and Adaptation: Canadian Pre-Service Teachers’ Reciprocal Learning Experience
Bibliographic record
Abstract
This chapter investigates the culture shock and adaptation processes experienced by eight Canadian pre-service teachers during a two-week international internship in Beijing, China, as part of the Teacher Education Reciprocal Learning Program (RLP) among the University of Windsor (UW), Beijing Foreign Studies University (BFSU), and Southwest University (SWU). By utilizing narrative inquiry, the authors delve into the cross-cultural encounters and adjustments of these pre-service teachers, highlighting their initial cultural disequilibrium, particularly in food culture, and their subsequent adaptation efforts. The narrative reveals the challenges faced by these teachers, such as navigating dietary restrictions and learning Chinese dining etiquette, as well as their engagement with Chinese school culture and history. Through these experiences, the pre-service teachers not only navigated their culture shock but also began appreciating the depth of Chinese culture and education system and developed a better understanding of the importance of bicultural education in bilingual education. The study extends its analysis to the reciprocal learning and reflections of both the Canadian pre-service teachers and the authors, who served as program facilitators, thereby enriching the dialogue on cross-cultural understanding in teacher education. This chapter contributes to the literature on international teacher education by offering insights into the complex dynamics of culture shock, adaptation, and reciprocal learning within a Sino-Canadian educational context.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.048 | 0.019 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".