Developmental patterns of non-word repetition by monolingual and bilingual school-aged children
Bibliographic record
Abstract
PURPOSE: The present study examines cross-sectional trends in performance on a quasi-universal non-word repetition (NWR) task. It also considers whether NWR performance is dependent on levels of exposure to a language and compares the performance of bilingual children across their two languages. METHOD: A cross-sectional design was employed. The English and French quasi-universal NWR tasks, featuring stimuli from two to five syllables in length, was administered to Canadian school-aged monolinguals and two groups of bilinguals: those who encountered their second language in early childhood and those who encountered their second language at school entry. RESULT: When evaluated in English, the early-exposure bilinguals produced significantly fewer errors than the school-entry exposure bilinguals and the English monolingual groups. When evaluated in French, the early-exposure bilinguals and the French monolinguals produced significantly fewer errors than the school-entry exposure bilinguals. Compared across languages, the French monolinguals produced fewer errors on the French version of the NWR task than the English monolinguals did on the English version of the same task. In both languages and across all analyses, the youngest age group (7-8 years) produced more errors than the two older groups (9-10 years and 11-12 years). CONCLUSION: The quasi-universal NWR task showed sensitivity to improvements from 7 to 11 years of age in English and up to 9 years of age in French. Better performance in French may be due to the relatively greater frequency of multisyllabic words in that language. The development and use of this particular NWR task with monolinguals and bilinguals-in both of their languages-contribute to a deeper understanding of quasi-universal NWR performance in typically developing 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".