Patterns of language switching and bilingual children’s word learning: An experiment across two communities
Bibliographic record
Abstract
Language switching is common in bilingual environments, including those of many bilingual children. Some bilingual children hear rapid switching that involves immediate translation of words (an ‘immediate-translation’ pattern), while others hear their languages most often in long blocks of a single language (a ‘one-language-at-a-time’ pattern). Our two-site experimental study compared two groups of developing bilinguals from different communities, and investigated whether differences in the timing of language switching impose different demands on bilingual children’ learning of novel nouns in their two languages: do children learn differently if they hear a translation immediately vs. if they hear translations more separated in time? Using an at-home online tablet word learning task, data were collected asynchronously from 3- to 5-year-old bilinguals from French–English bilingual families in Montreal, Canada (N = 31) and Spanish–English bilingual families in New Jersey, USA (N = 22). Results showed that bilingual children in both communities readily learned new words, and their performance was similar across the immediate-translation and one-language-at-a-time conditions. Our findings highlight that different types of bilingual interactions can provide equal learning opportunities for bilingual children’s vocabulary 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".