English Verb Accuracy of Bilingual Cantonese–English Preschoolers
Bibliographic record
Abstract
Purpose: Knowledge of verb development in typically developing bilingual preschoolers may inform clinicians about verb accuracy rates during the 1st 2 years of English instruction. This study aimed to investigate tensed verb accuracy in 2 assessment contexts in 4- and 5-year-old Cantonese-English bilingual preschoolers. Method: The sample included 47 Cantonese-English bilinguals enrolled in English preschools. Half of the children were in their 1st 4 months of English language exposure, and half had completed 1 year and 4 months of exposure to English. Data were obtained from the Test of Early Grammatical Impairment (Rice & Wexler, 2001) and from a narrative generated in English. Results: By the 2nd year of formal exposure to English, children in the present study approximated 33% accuracy of tensed verbs in a formal testing context versus 61% in a narrative context. The use of the English verb BE approximated mastery. Predictors of English third-person singular verb accuracy were task, grade, English expressive vocabulary, and lemma frequency. Conclusions: Verb tense accuracy was low across both groups, but a precocious mastery of BE was observed. The results of the present study suggest that speech-language pathologists may consider, in addition to an elicitation task, evaluating the use of verbs during narratives in bilingual Cantonese-English bilingual children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".