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Record W4229445721 · doi:10.1080/13670050.2022.2062217

The role of exposure on school-aged bilingual language abilities – it depends on what you measure

2022· article· en· W4229445721 on OpenAlexafffund
Myriam L. H. Beauchamp, Andrea A. N. MacLeod

Bibliographic record

VenueInternational Journal of Bilingual Education and Bilingualism · 2022
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of AlbertaMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNeuroscience of multilingualismPsychologyDevelopmental psychologyLanguage developmentLanguage proficiencyMathematics education

Abstract

fetched live from OpenAlex

Few studies have focused on bilingual language development in school-aged simultaneous bilingual children. The current study tackles this gap in the literature by examining the influence of language exposure on the development of both languages in school-aged simultaneous bilingual living in additive bilingual contexts. The language abilities of 19 school-aged simultaneous bilingual children were assessed using standardised language measures in French. Their amount of language exposure was also measured using a parent questionnaire. No correlation was found between language abilities in French and exposure to French. Results did, however, show that most of the children who received as little as 20% of their lifetime exposure to French, obtained scores within the average range on all of the measures. Additionally, children who were exposed to both of their languages on a daily basis were more likely to have better performances than their peers who used one of their languages less frequently. This effect was observed even though children in the former group received less exposure to their strongest language than children who were not exposed to both of their languages daily. Therefore, while exposure is important, other factors may have a positive influence on simultaneous bilinguals’ language proficiency levels.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.326
Teacher spread0.315 · 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.

Study designQualitative
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

Citations6
Published2022
Admission routes2
Has abstractyes

Explore more

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