Canadian-born trilingual children’s narrative skills in their heritage language and Canada’s official languages
Bibliographic record
Abstract
This study examines the narrative production of 12 trilingual children aged 8;7–11;10 in three languages: heritage Romanian as a first language, mainstream English as a second, and school French as a third. Narrative macrostructure was analyzed via the Narrative Structure Scheme, while microstructure was assessed via story length, lexical diversity, and subordination index. An additional microstructural measure was Guiraud’s index of lexical richness. Results were only partially compatible with monolingual or bilingual findings. Analyses demonstrated that: (i) group macrostructural strength was equal across languages but only as a central tendency; and (ii) while the correlation between Romanian and English macrostructure almost achieved significance, neither was related to French scores. Contrary to the findings of Heilmann et al.’s monolingual study, no microstructural component correlated with macrostructure. Within microstructure, there was no significant difference in sentence complexity (measured through the subordination index) across languages, but scores for lexical diversity and Guiraud’s index were lower in French than in Romanian and English. The findings point to distinctions between trilinguals and both bilinguals and monolinguals, and the possible problem with testing trilinguals for language proficiency or disorders using instruments created for monolinguals.Trial registration: Netherlands National Trial Register identifier: ntr-.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".