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Record W2303349340 · doi:10.1177/1367006915613161

French–English bilingual children’s tense use and shift in narration

2015· article· en· W2303349340 on OpenAlexaff
Huong T. T. Hoang, Elena Nicoladis, Lisa Smithson, Reyhan Furman

Bibliographic record

VenueInternational Journal of Bilingualism · 2015
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNeuroscience of multilingualismPsychologyNarrativeLinguisticsContext (archaeology)Present tenseStyle (visual arts)Past tenseVerbHistoryArtLiterature

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.318
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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Same venueInternational Journal of BilingualismSame topicLanguage Development and DisordersFrench-language works237,207