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Record W2221129924 · doi:10.1093/pch/11.9.577

Multiple literacies: Linking the research on bilingualism and biliteracies to the practical

2006· article· en· W2221129924 on OpenAlexaff
Therese Dufresne, Diana Masny

Bibliographic record

VenuePaediatrics & Child Health · 2006
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of OttawaWilfrid Laurier University
Fundersnot available
KeywordsNeuroscience of multilingualismReading (process)PsychologyLiteracyLanguage developmentEarly childhoodLinguisticsPromotion (chess)Developmental psychologyPedagogyPolitical science

Abstract

fetched live from OpenAlex

The present article addresses the fundamental developmental differences between monolingualism and bilingualism, as outlined in the literature. It takes into account research using qualitative and quantitative methodologies and reviews the advantages and possible disadvantages of bilingualism in preschool children. It then moves on to biliteracies in early childhood education. The questions addressed are: Will the early learning of a second language delay a child's language development? What effect does bilingualism have on the development of phonemic awareness, and does that have an impact on reading development? Do bilingual children have an advantage over monolingual children? What happens when a child with language impairment is called upon to develop two languages? What are some challenges regarding the acquisition of biliteracies? The present article concludes with recommendations for physicians who counsel bilingual families regarding early childhood literacy promotion and development.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0030.015
Scholarly communication0.0050.006
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.071
GPT teacher head0.425
Teacher spread0.354 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2006
Admission routes1
Has abstractyes

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