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Record W4387133700 · doi:10.31234/osf.io/uv3tw

DRAFT: Like Mother Like Child: Differential Impact of Mothers’ and Fathers’ Individual Language Use on Bilingual Language Exposure

2023· preprint· en· W4387133700 on OpenAlexaboutno aff
Andrea Sander‐Montant, Krista Byers‐Heinlein, Rébecca Bissonnette

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsHeritage languageEthnic groupPsychologyFirst languageDevelopmental psychologyNeuroscience of multilingualismLanguage assessmentLinguisticsSociologyMathematics educationPedagogy

Abstract

fetched live from OpenAlex

Language exposure is a determiner of language outcomes in bilingual children. The predictiveness of family language strategies versus parents’ individual language use on language exposure was contrasted in children aged 4-31 months (50% female) living in Montreal (mostly mid-high SES, European ethnicity). 221 were learning two community languages (French and English) and 60 were learning a community and a heritage language. Parents’ individual language use better predicted language exposure than family language strategies, and mothers had at least double the impact on language exposure than fathers, likely due to gendered caregiving roles. In a subset of families followed longitudinally, ~25% showed changes in strategy and individual language use over time. Individual caregivers, especially mothers, strongly shape bilingual children’s language exposure.

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)0.003
metaresearch head score (Gemma)0.027
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1040.024

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 head0.031
GPT teacher head0.325
Teacher spread0.294 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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Same topicLanguage Development and DisordersFrench-language works237,207