English Language Learners of Chinese Immigrant Families in Canadian Schools
Bibliographic record
Abstract
Given the context that many Chinese immigrant families are settling in Canada, their children, as English language learners (ELLs), have encountered a variety of obstacles and barriers while studying at local schools. The purpose of this Major Research Paper (MRP) is to understand the obstacles and barriers that Chinese newcomer ELLs in Ontario have encountered and hence to offer some helpful suggestions. A series of official documents of Ontario ELL curriculum has been introduced to school educators by the Ontario Ministry of Education (2005, 2008), such as Many Roots, Many Voices: Supporting English Language Learners in Every Classroom and Support English Language Learners Grade 1 to 8. Meanwhile, Chinese newcomer English learners have accepted systemic English language education in China before immigration. Therefore, to better understand the needs and challenges of Chinese newcomer ELLs, English curriculum in compulsory education in China and educational documents for ELLs in Ontario Canada will be discussed in this paper, followed by a comparison of pedagogies applied in compulsory education in Canada and China. Comparing the similarities and differences, and giving suggestions to pre-service, and in-service educators may help ELLs and EFLs to improve their academic performance. Other literature in the field is concerned with Chinese ELLs in Ontario elementary schools and the obstacles that they face at school. However, only a few studies compare ELL curriculum documents in Canada and English curricula standards in China, then give suggestions to schoolteachers, parents, and students' home countries about the challenges faced by Chinese immigrant children. Those barriers and challenges reflect the problems that still exist in curriculum and educational policies. Therefore, it is important to introduce Chinese EFLs to Ontario teachers in order to help Chinese ELLs get involved in their unfamiliar environment
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".