Parental Language Mixing in Montreal: Rates, Predictors, and Relation to Infants’ Vocabulary Size
Bibliographic record
Abstract
Language mixing is a common feature of bilingual communication, yet its predictors and effects on children's vocabulary development remain debated. Most research has been conducted in contexts with clear societal and heritage languages, leaving open questions about language mixing in environments with two societal languages. Montreal provides a unique opportunity to examine this question, as both French and English hold societal status, while many families also maintain heritage languages. Using archival data from 398 bilingual children (7-34 months), we looked at French-English bilinguals (representing societal bilingualism) and heritage-language bilinguals within the same sociolinguistic environment. We assessed the prevalence, predictors, and motivations of parental language mixing and its relationship with vocabulary development. Results revealed that mixing was less frequent among French-English bilinguals compared to heritage-language bilinguals in the same city. The direction of mixing differed between groups: French-English bilinguals mixed based on language dominance, while heritage-language bilinguals mixed based on societal language status. Primary motivations included uncertainty about word meanings, lack of suitable translations, and teaching new words. Mixing showed minimal associations with vocabulary size across participants. These findings suggest that parental mixing practices reflect adaptive strategies that vary by sociolinguistic context rather than detrimental influences on early language acquisition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".