Does Early Exposure to Chinese–English Biliteracy Enhance Cognitive Skills?
Bibliographic record
Abstract
Clarifying the effects of biliteracy on cognitive development is important to understanding the role of cognitive development in L2 learning. A substantial body of research has shed light on the cognitive factors contributing to biliteracy development. Yet, not much is known about the effect of the degree of exposure to biliteracy on cognitive functions. To fill this research void, we measured three categories of biliteracy skills (language, reading, and cognitive-linguistic skills in both Chinese and English) jointly and investigated the effects of biliteracy skill performance in these three categories on cognitive skills (working memory and attentional control) in Chinese L1 children who were exposed to English as L2 beginning at age 3 ("early") or in grade 3 ("late"). In this cross-sectional study, 10 parallel Chinese and English language, reading, and cognitive-linguistic measures were administered to emerging Chinese-English bilingual children in grade 3 (n = 178) and grade 6 (n = 176). The results revealed that early exposure to Chinese-English biliteracy enhanced cognitive skills but with a cost of a slight delay in performance on Chinese L1 language skills in grade 3 (but not in grade 6). Importantly, the present findings suggest that, in addition to universal and developmental processes, the cumulative effect of early and sustained bi-scriptal exposure enhances working memory and attention in school children.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".