Narratives in English and Cantonese (Rezzonico et al., 2016)
Bibliographic record
Abstract
<b>Purpose: </b>The aim of this study was to compare narratives generated by 4-year-old and 5-year-old children who were bilingual in English and Cantonese.<b>Method: </b>The sample included 47 children (23 who were 4 years old and 24 who were 5 years old) living in Toronto, Ontario, Canada, who spoke both Cantonese and English. The participants spoke and heard predominantly Cantonese in the home. Participants generated a story in English and Cantonese by using a wordless picture book; language order was counterbalanced. Data were transcribed and coded for story grammar, morphosyntactic quality, mean length of utterance in words, and the number of different words. <b>Results: </b>Repeated measures analysis of variance revealed higher story grammar scores in English than in Cantonese, but no other significant main effects of language were observed. Analyses also revealed that older children had higher story grammar, mean length of utterance in words, and morphosyntactic quality scores than younger children in both languages. Hierarchical regressions indicated that Cantonese story grammar predicted English story grammar and Cantonese microstructure predicted English microstructure. However, no correlation was observed between Cantonese and English morphosyntactic quality.<b>Conclusions: </b>The results of this study have implications for speech-language pathologists who collect narratives in Cantonese and English from bilingual preschoolers. The results suggest that there is a possible transfer in narrative abilities between the two languages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.296 | 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; both teacher heads agree on what is shown here.
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".