A Narrative Inquiry into Young Chinese English Language Learners’ Cross-cultural Experiences Between Canada and China
Bibliographic record
Abstract
Due to the impacts of internationalization and competition within the global knowledge economy, China has consistently been the leading country to send the highest number of visiting scholars abroad (Institute of International Education, 2018; Ai, 2019), but the group of Chinese visiting scholars’ children is often ignored. Will they face similar challenges as Chinese international students or immigrant children? How do they feel during the short stay in Canada and after they go back to China? As young ELLs, how do they adapt to the unfamiliar environment through language and culture? Therefore this research aims to fill the gap by making a narrative inquiry into the cross-cultural experiences of five young Chinese ELLs between Canada and China. The main research purposes are: 1) to understand how the young Chinese ELLs make meaning of their cross-cultural experiences through language and culture; 2) to explore the role that translanguaging plays in the transnational trip, including its changes across time; 3) to reveal the impact of the cross-cultural experiences on Chinese children’s language practices and intercultural communication. In that way, the research not only addresses specific questions but also grasps a broader picture of Chinese children’s transnational trip.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".