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Multilingualism

2020· other· en· W4256279491 on OpenAlexaff
Christopher T. Fennell

Bibliographic record

VenueThe Encyclopedia of Child and Adolescent Development · 2020
Typeother
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMultilingualismNeuroscience of multilingualismLanguage developmentPsychologyLanguage acquisitionCognitionSecond languageLinguisticsFirst languageControl (management)Developmental psychologyCognitive psychologyComputer scienceArtificial intelligenceMathematics educationPedagogy

Abstract

fetched live from OpenAlex

Despite the global prevalence of children learning more than one language, childhood multilingualism studies has taken off in earnest only since the late 1980s, with the vast majority of that work examining children who have acquired two languages from birth, or simultaneous bilinguals. Overall, global measures of language development (e.g., developmental milestones, total language scores) typically do not differ between bilinguals and monolinguals. However, the development of each language of the multilingual is closely tied to exposure to the specific language. Children learning a second language later in toddlerhood, or sequential bilinguals, are not as well researched, but their development and the factors associated with better second‐language acquisition are also summarized. Finally, outside of language, there is some evidence that the cognitive control associated with navigating multiple languages can lead to bilingual advantages in general cognitive control, even in infancy.

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)0.000
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0200.002

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 head0.010
GPT teacher head0.253
Teacher spread0.244 · 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
GenreOther

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

Citations0
Published2020
Admission routes1
Has abstractyes

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