Chinese Pre-Service Students’ Cross-Cultural Perceptions on Canadian Generalist Teachers Contextualized in Reciprocal Learning Program: A Narrative Inquiry
Bibliographic record
Abstract
In Canadian elementary schools, teachers are under increasing pressure to be generalist practitioners in all subjects which are becoming more and more specialized and complex based on elementary curriculum (Pollock & Minszak, 2015) and at same time they are under pressure to be specialist teachers in certain subject areas such as math(Stokke, 2015). According to Ontario College of Teachers, “Ontario applicants who complete their application for a certificate of qualification and registration on or after March 31, 2020 must pass a Mathematics Proficiency Test in order to become certified, whether or not the application was started before that date”. How Chinese pre-service teachers from a different teaching tradition view a totally different teaching tradition can provide valuable insights to Canadian policy makers and educators, especially when high-risk test and accountability are highly stressed in Canada. This study is to explore Chinese pre-service teachers’ cross-cultural perceptions on generalist teachers in Canadian elementary schools when they did their cross-cultural placement through Reciprocal Learning Program with narrative inquiry as its methodology. Reciprocal Learning as Partnership (Xu & Connelly, 2017) shapes the theoretical framework. Chinese students’ weekly reflection, weekly debriefing and casual talks are the data of this study and thematic data analysis is the method of data analysis. The results show that from Chinese pre-service teachers’ cross-cultural understanding, generalist and specialist teachers have both advantages and disadvantages. Chinese teachers need to be cautioned to borrow this type of teaching method, and Canadian teachers need to be more specialized in some subjects such as math and science.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".