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Record W4389683068 · doi:10.1017/s0305000923000648

Cognates are advantaged over non-cognates in early bilingual expressive vocabulary development

2023· article· en· W4389683068 on OpenAlexaff
Lori Mitchell, Rachel Ka Ying Tsui, Krista Byers‐Heinlein

Bibliographic record

VenueJournal of Child Language · 2023
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia University
FundersNational Institutes of Health
KeywordsPsychologyVocabularyLinguisticsVocabulary developmentLanguage developmentNeuroscience of multilingualismDevelopmental psychology

Abstract

fetched live from OpenAlex

) are called cognates. Research has consistently shown that children and adults process and name cognates more easily than non-cognates. The present study explored if there is such an advantage for cognate production in bilinguals' early vocabulary development. Longitudinal expressive vocabulary data were collected from 47 English-French bilinguals starting at 16-20 months up to 27 months (a total of 219 monthly administrations in both English and French). Children produced a greater proportion of cognates than non-cognates, and the interval between producing a word and its translation equivalent was about 10-15 days shorter for cognates than for non-cognates. The findings suggest that cognate learning is facilitated in early bilingual vocabulary development, such that phonological overlap supports bilinguals in learning phonologically similar words across their two languages.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.289
Teacher spread0.282 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

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