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Record W3160872202 · doi:10.1017/s0305000921000118

Code-switching in parents’ everyday speech to bilingual infants

2021· article· en· W3160872202 on OpenAlexaffabout
Lena V. Kremin, JÚLIA CRISTINA SOUZA ALVES, Adriel John Orena, Linda Polka, Krista Byers‐Heinlein

Bibliographic record

VenueJournal of Child Language · 2021
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsConcordia UniversityMcGill UniversityUniversity of British ColumbiaCentre for Research on Brain Language and Music
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsCode-switchingPsychologyVocabularyNeuroscience of multilingualismCode (set theory)SentenceLinguisticsDevelopmental psychologyFirst languageComputer scienceProgramming language

Abstract

fetched live from OpenAlex

Code-switching is a common phenomenon in bilingual communities, but little is known about bilingual parents' code-switching when speaking to their infants. In a pre-registered study, we identified instances of code-switching in day-long at-home audio recordings of 21 French-English bilingual families in Montreal, Canada, who provided recordings when their infant was 10 and 18 months old. Overall, rates of infant-directed code-switching were low, averaging 7 times per hour (6 times per 1,000 words) at 10 months and increasing to 28 times per hour (18 times per 1,000 words) at 18 months. Parents code-switched more between sentences than within a sentence; this pattern was even more pronounced when infants were 18 months than when they were 10 months. The most common apparent reasons for code-switching were to bolster their infant's understanding and to teach vocabulary words. Combined, these results suggest that bilingual parents code-switch in ways that support successful bilingual language acquisition.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.010
GPT teacher head0.312
Teacher spread0.302 · 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

Citations59
Published2021
Admission routes2
Has abstractyes

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Same venueJournal of Child LanguageSame topicLanguage Development and DisordersFrench-language works237,207