When Learners Read in Two Languages: Understanding Chinese-English Bilingual Readers Through Miscue Analysis
Bibliographic record
Abstract
The number of Chinese-speaking students in Canadian schools is increasing dramatically. This article discusses a study in which we explored reading processes in Chinese and English through examining children’s reading in both languages. Based in a socio-psycholinguistic framework (K. Goodman, Wang, Iventosch, & Y. Goodman,2012; Kabuto, 2017) and through using miscue analysis, we examined how children apply their knowledge of language to Mandarin and English reading. This qualitative research included interviews with four Chinese-English bilingual children between grades 3 and 5 in an urban center as well as the analysis of their reading performance in both languages. From a comparative perspective, we discuss some of the similarities and differences between these two different orthographic language systems by offering syntactic comparisons of the two languages through psycholinguistic language cueing systems. We believe that knowing about how Chinese and English readers construct meaning in both languages will help English as an Additional Language (EAL) teachers, in fact all classroom teachers, to teach reading to bilingual and biliterate children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".