French–English bilingual children’s tense use and shift in narration
Bibliographic record
Abstract
Bilingual children sometimes show delays relative to monolinguals on language tasks. In the present studies, we explored whether French–English bilinguals’ tense use and shift would show a developmental lag in the context of narration. In Study 1, we showed that both French and English monolinguals showed age-related changes in tense use, with preschoolers preferring the past and adults the present. A developmental lag among bilingual children could therefore take the form of prolonged use of the past tense through middle childhood. In Study 2, we observed tense use in the narratives of French–English bilingual children (8–10 years), as well as French and English monolinguals from the same age group. The bilinguals tended to use more present tense than the monolinguals. In qualitative analyses, bilinguals also used a multitude of expressive strategies, such as exclamations, repetitions and onomatopoeia, that made the stories more vivid. Taken together these results suggest that French–English bilinguals do not present developmental differences from monolinguals in tense use. Instead, they adopt an imagistic narrative style that differs from the monolinguals in multiple ways, including a greater use of the present tense. The adoption of this style might be linked to both bilingualism and a cultural preference among French–English bilinguals.
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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 | 0.000 |
| 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) | 0.004 | 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".