Language Exposure in Bilingual Toddlers: Performance on Nonword Repetition and Lexical Tasks
Bibliographic record
Abstract
PURPOSE: The amount of language exposure is correlated with bilingual lexical development, but findings are mixed on how exposure relates to nonword repetition (NWR), a complex skill involving both short-term processing and long-term vocabulary knowledge. We extend previous work to a younger age group by investigating the role of exposure on NWR versus vocabulary, along with the effect of item construction and scoring. METHOD: Sixty typically developing children (ages 2;5-3;6[years;months]) were assessed for NWR and receptive and expressive vocabulary. Participants ranged in amount of previous exposure to English and French from 0% to 100% and were tested in both languages if able to participate, even with very limited exposure (28 completed testing in both languages, 11 completed testing in English only, 21 completed testing in French only). RESULTS: Correlational analyses showed moderate to strong associations between the amount of exposure and vocabulary in that language, whereas the relationship of exposure with NWR was weak or nonsignificant, depending on scoring method. NWR correlated with vocabulary in English only. Performance on NWR was affected by nonword length but unaffected by wordlikeness. CONCLUSIONS: NWR and vocabulary were differently related to language exposure. The underlying mechanisms of NWR at this age appeared mainly reliant on short-term processes, in contrast to long-term vocabulary knowledge.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".