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Record W2474769562 · doi:10.1017/s0142716416000199

Minimal pair word learning and vocabulary size: Links with later language skills

2016· article· en· W2474769562 on OpenAlexafffund
Nenagh Kemp, JULIANNE SCOTT, Barbara May Bernhardt, Carolyn E. Johnson, Linda S. Siegel, Janet F. Werker

Bibliographic record

VenueApplied Psycholinguistics · 2016
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsVocabularyPsychologyVocabulary developmentTask (project management)PhonologyLanguage developmentWord (group theory)Language productionLinguisticsCognitive psychologyDevelopmental psychologyCognition

Abstract

fetched live from OpenAlex

ABSTRACT There is increasing interest in the link between early linguistic skills and later language development. In a longitudinal study, we investigated infants’ (a) ability to use speech sound categories to guide word learning in the habituation-based minimal pair switch task, and (b) early productive vocabulary, related to their concurrent and later language task performance. The participants at Phase 1 were 64 infants aged 16–24 months (25 with familial risk of language/speech impairment), followed up at 27 months (Phase 2) and at 3 years (Phase 3). Phase 1 productive vocabulary was correlated with Phase 2 productive vocabulary, and with concurrent and later (Phase 3) tests of language production and comprehension scores (standardized tool), and phonology. Phase 1 switch task performance was correlated with concurrent productive vocabulary and language production scores, but not by Phase 3. However, a combination of early low vocabulary score and a preference for looking at an already-habituated word–object combination in the switch task may show some promise as an identifier for early speech–language intervention. We discuss how these relations can help us better understand the foundations of word learning.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.999

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)0.0020.000

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 head0.005
GPT teacher head0.265
Teacher spread0.260 · 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
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

Citations16
Published2016
Admission routes2
Has abstractyes

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