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New Discoveries From the Bilingual Brain and Mind Across the Life Span: Implications for Education

2009· article· en· W1999424534 on OpenAlexaff
Laura‐Ann Petitto

Bibliographic record

VenueMind Brain and Education · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsPsychologyReading (process)LiteracyNeuroscience of multilingualismDevelopmental psychologyBilingual educationDual languageDevelopmental cognitive neuroscienceSocioeconomic statusCognitive psychologyCognitionLinguisticsMathematics educationCognitive neuroscienceNeurosciencePedagogyMedicine

Abstract

fetched live from OpenAlex

ABSTRACT We discuss the fruits of educational neuroscience research from our laboratory and show how the typical maturational timing milestones in bilingual language acquisition provide educators with a tool for differentiating a bilingual child experiencing language and reading delay versus deviance. Further, early schooling in two languages simultaneously affords young bilingual children a reading advantage and may also ameliorate the negative effect of low socioeconomic status on literacy. Using powerful brain imaging technology, functional Near Infrared Spectroscopy, we provide a first‐time look into the developing brains of bilingual as comapred to monolingual children. We show unequivocally that the age of first bilingual exposure is a vital predictor of bilingual language and reading mastery. Accounts that promote later dual language and reading instruction, or those that assert human brain development is unrelated to bilingual language mastery, are not supported by the present findings. We discuss the implications for education, teachers, and developmental brain sciences.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.032
GPT teacher head0.351
Teacher spread0.319 · 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 designOther design
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

Citations87
Published2009
Admission routes1
Has abstractyes

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