How Chinese–English Bilingual Fourth Graders Draw on Syntactic Awareness in Reading Comprehension: Within‐ and Cross‐Language Effects
Bibliographic record
Abstract
ABSTRACT Syntax, or sentence structure, plays a role in reading comprehension, but how students draw on their awareness of syntax in their reading remains unclear; the mechanism is even more ambiguous in bilingual students. In this study, we evaluated the direct and indirect contributions of syntactic awareness on first‐language Chinese and second‐language English reading comprehension among 227 Hong Kong Chinese–English bilingual fourth graders. We designed language‐shared and language‐unique tasks of syntactic awareness, assessed reading comprehension in both Chinese and English, and took other reading‐related cognitive and metalinguistic measures. We found a statistically significant direct effect of syntactic awareness on reading comprehension within both first‐language Chinese and second‐language English, along with indirect effects via word reading. Moreover, in their reading comprehension within both English and Chinese, students drew on awareness of syntactic features that are shared between English and Chinese more than those unique to either language. Students were also generally more accurate with language‐shared than language‐unique items, further pointing to the possibility of transfer. Our findings clarify dual roles for syntactic awareness in reading comprehension in Chinese and English, as well as transfer of awareness of syntactic structures in the two typologically distinct languages. We discuss these results in relation to theories of both reading comprehension and transfer.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".