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Record W2144401259 · doi:10.36510/learnland.v7i1.632

Bilingualism in the Early Years: What the Science Says

2013· article· en· W2144401259 on OpenAlexaffvenue
Krista Byers‐Heinlein, Casey Lew‐Williams

Bibliographic record

VenueLEARNing Landscapes · 2013
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia University
FundersNational Institute of Child Health and Human DevelopmentEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentFondation Pour l'AuditionAmerican Speech-Language-Hearing Foundation
KeywordsNeuroscience of multilingualismMythologyPsychologyLinguisticsDevelopmental psychologyHistoryClassicsPhilosophy

Abstract

fetched live from OpenAlex

Many children in North America and around the world grow up exposed to two languages from an early age. Parents of bilingual infants and toddlers have important questions about the costs and benefits of early bilingualism, and how to best support language acquisition in their children. Here, we separate common myths from scientific findings to answer six of parents' most common questions about early bilingual 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)0.004
metaresearch head score (Gemma)0.014
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 score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.010
Scholarly communication0.0050.010
Open science0.0010.002
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.274
Teacher spread0.261 · 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

Citations246
Published2013
Admission routes2
Has abstractyes

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