A Transdisciplinary Approach to Chinese and Japanese Language Teaching: Collaborative Pedagogy Across Languages, Disciplines, Communities, and Borders
Bibliographic record
Abstract
A Transdisciplinary Approach to Chinese and Japanese Language Teaching illustrates how the transdisciplinary approach to second language acquisition (SLA) centers around collaboration to provide a learning-conducive environment with rich semiotic resources for second/foreign language learners.The volume consists of 14 chapters from leading experts in SLA and Chinese and Japanese language educators from Canada, China, Japan, the United Kingdom, and the United States of America. As a first work of its kind, the contributions feature both theoretical interpretations of transdisciplinary concepts that can apply to Chinese/Japanese as a second language learning and case studies showcasing how college-level Chinese and Japanese language educators design and implement pedagogical projects in collaboration with partners across languages, disciplines, communities, and borders by adopting a transdisciplinary perspective to analyze students’ learning outcomes.This book will benefit researchers, administrators, educators, and teacher educators in higher education with an interest in world language education and interdisciplinary and project-based teaching.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| 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".