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Record W1995625004 · doi:10.1177/014272370002005801

The role of a child's productive vocabulary in the language choice of a bilingual family

2000· article· en· W1995625004 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFirst Language · 2000
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCode-mixingCode-switchingPortugueseLinguisticsContext (archaeology)VocabularyMixing (physics)PsychologyCode (set theory)Neuroscience of multilingualismDevelopmental psychologyComputer scienceHistorySet (abstract data type)Programming languagePhysics

Abstract

fetched live from OpenAlex

While bilingual children's code-mixing was once taken as a sign that they had confused their languages, many studies have now shown that bilingual children can differentiate their languages from very early in development. Why, then, do they code-mix? This study examined several factors that might contribute to a Brazilian Portuguese-English bilingual boy's code-mixing within the context of the family's language use. He was filmed every week in two observation sessions from age 1;0 to 1;6: one with his Portuguese-speaking father and one with his English-speaking mother. The results showed that about 90% of the child's code- mixing could be accounted for by lexical gaps in one language. Furthermore, the parents' code-mixing could often be accounted for by switching to use words that are in the child's productive vocabulary. These results are discussed in terms of the family's creative use of the child's limited linguistic resources.

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.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.006
GPT teacher head0.263
Teacher spread0.257 · 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