MétaCan
Menu
Back to cohort
Record W2757581098 · doi:10.1002/9781118829516.ch23

Language Comprehension in Monolingual and Bilingual Children

2017· other· en· W2757581098 on OpenAlexaff
Krista Byers‐Heinlein, Casey Lew‐Williams

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia University
FundersNational Institute of Child Health and Human DevelopmentNational Institutes of Health
KeywordsComprehensionFluencyVariety (cybernetics)VocabularyPsychologyLinguisticsTRACE (psycholinguistics)Language acquisitionComputer scienceCognitive psychologyArtificial intelligenceMathematics education

Abstract

fetched live from OpenAlex

Infants and toddlers grow up in a variety of language environments—for example, some are monolingual and some are bilingual—but nearly all children develop the ability to understand the language(s) around them. In this chapter, we trace children's path to language comprehension, from listening in infancy, to phonetic perception, speech segmentation, word learning, vocabulary development, and finally understanding language in real time. For both monolinguals and bilinguals, developing fluency in language comprehension in infancy and toddlerhood sets the stage for later language and school success.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.284
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.316
Teacher spread0.305 · 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.

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

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicLanguage Development and DisordersFrench-language works237,207