Bilingual children's long‐term outcomes in English as a second language: language environment factors shape individual differences in catching up with monolinguals
Bibliographic record
Abstract
Bilingual children experience more variation in their language environment than monolingual children and this impacts their rate of language development with respect to monolinguals. How long it takes for bilingual children learning English as a second language (L2) to display similar abilities to monolingual age-peers has been estimated to be 4-6 years, but conflicting findings suggest that even 6 years in school is not enough. Most studies on long-term L2 development have focused on just one linguistic sub-domain, vocabulary, and have not included multiple individual difference factors. For the present study, Chinese first language-English L2 children were given standardized measures of vocabulary, grammar and global comprehension every year from 4 ½ to 6 ½ years of English in school (ages 8½ to 10½); language environment factors were obtained through an extensive parent questionnaire. Children converged on monolingual norms differentially according to the test, with the majority of children reaching monolingual levels of performance on the majority of tests by 5 ½ years of English exposure. Individual differences in outcomes were predicted by length of English exposure, mother's education, mother's English fluency, child's use of English in the home, richness/quality of the English input outside school and age of arrival in Canada. In sum, the timeframe for bilinguals to catch up to monolinguals depends on linguistic sub-domain, task difficulty and on individual children's language environment, making 4-6 years an approximate estimate only. This study also shows that language environment factors shape not only early-stage but also late-stage bilingual development.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".