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Record W1955116558 · doi:10.1111/lang.12055

Languages As Categories: Reframing the “One Language or Two” Question in Early Bilingual Development

2014· article· en· W1955116558 on OpenAlexafffund
Krista Byers‐Heinlein

Bibliographic record

VenueLanguage Learning · 2014
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNeuroscience of multilingualismLinguisticsPsychologyContext (archaeology)Cognitive reframingLanguage developmentDevelopmental linguisticsComprehension approachDevelopmental psychologyLanguage educationMathematics education

Abstract

fetched live from OpenAlex

One of the most enduring questions in the field of bilingualism is whether bilingual infants and children initially have one language system or two. Research with adults indicates that, while bilinguals do not represent their languages in two fully encapsulated language systems, they are able to functionally differentiate their languages. This article proposes that bilinguals differentiate their languages insofar as they can treat elements of their languages as belonging to different categories. Several lines of research with bilingual adults and children are considered in the context of perceptual and conceptual language categories. The article ends with a discussion of how language categories might emerge over the course of early bilingual development and outlines directions for future research.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.024
Scholarly communication0.0040.011
Open science0.0010.004
Research integrity0.0010.003
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 head0.322
Teacher spread0.311 · 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 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

Citations50
Published2014
Admission routes2
Has abstractyes

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