Researching Chinese Learners: skills, perceptions, intercultural adaptations
Bibliographic record
Abstract
China has the largest education system and the largest number of learners of English. Chinese students form the largest single group of international learners in many countries. However, there are few international studies of how Chinese students learn and adapt to other learning cultures. \n \nThis book focuses on three themes related to Chinese learners \n \nthe teaching and learning of English in Chinese classrooms \nintercultural adaptation of Chinese learners in international educational contexts \nthe application of a range of research approaches and innovative methodologies \n \n \nThe authors, from Australia, Canada, China, Hong Kong, New Zealand and the UK, present original research with Chinese learners in higher education which provides a rich and wide international context for understanding social, cultural and academic issues related to Chinese students. \n \nThe book should be of interest to researchers, teachers and postgraduate students in applied linguistics and English teaching, intercultural adaptation and international education. \n \nBook Description \nOriginal studies on Chinese learners in China and in the West using a large variety of research methods \nAbout the Author \nLIXIAN JIN is Reader in Linguistics & Health Communication and the Director for the Centre of Intercultural Research in Communication and Learning (CIRCL) at De Montfort University, UK. \n \nMARTIN CORTAZZI is a Visiting Professor in the Centre of Applied Linguistics at the University of Warwick, UK. \n \nDR JIN and PROFESSOR CORTAZZI are visiting professors at several key universities in China and have been conducting joint research into Chinese and other learners of English for over twenty years by using a variety of research methods including surveys, ethnography, narrative and metaphor analysis. Their research, with over 100 publications, has been widely recognized by researchers and university teachers from the West and in East Asia. They are the series editor and cultural editor for College English textbooks: Creative Reading and Creative Communication, jointly published by Macmillan publishers and Shanghai Foreign Language Education Press. They have also published a series of teachers' books for New Standard College English textbooks by Macmillan publishers and Beijing Foreign Language Teaching & Research Press.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".