A Narrative Study of the Growth of the Graduates of Science Team Based on Reciprocal Learning in Teacher Education and the School Education between China and Canada Program.
Bibliographic record
Abstract
Reciprocal Learning in Teacher Education and School Education between China and Canada?the following called RLP? covers six areas: general education, teacher education, science education, math education, language and information education. One of the characteristics of the program is the participation of postgraduates and the growth they have gotten. The postgraduate is not only the participant of the program but also the object of study. In this study, the graduate team of the China Science Team is taken as the case and the growth of the postgraduates is taken as the main line. The narrative study method is used to describe the progress of team learning in the program, communicating in the program, studying in the program and rethinking in the program, with expectation to providing a model for pre-service teacher education, especially post-graduate education, based on program learning. Conclusion: Reciprocal Learning in Teacher Education and School Education between China and Canada has not only effectively promoted the exchange and reciprocal learning in school education and teacher education between China and Canada, but also provided a platform for postgraduates to learn, research and grow.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".