Effects of language mixing on bilingual children's word learning
Bibliographic record
Abstract
Abstract Language mixing is common in bilingual children's learning environments. Here, we investigated effects of language mixing on children's learning of new words. We tested two groups of 3-year-old bilinguals: French–English (Experiment 1) and Spanish–English (Experiment 2). Children were taught two novel words, one in single-language sentences (“Look! Do you see the dog on the teelo?”) and one in mixed-language sentences with a mid-sentence language switch (“Look! Do you see thechien/perroon the walem?”). During the learning phase, children correctly identified novel targets when hearing both single-language and mixed-language sentences. However, at test, French–English bilinguals did not successfully recognize the word encountered in mixed-language sentences. Spanish–English bilinguals failed to recognize either word, which underscores the importance of examining multiple bilingual populations. This research suggests that language mixing may sometimes hinder children's encoding of novel words that occur downstream, but leaves open several possible underlying mechanisms.
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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) | 0.001 | 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 | 0.001 |
| 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".