The relationship between bilingual exposure and vocabulary development
Bibliographic record
Abstract
The relationship between amount of bilingual exposure and performance in receptive and expressive vocabulary in French and English was examined in 5-year-old Montreal children acquiring French and English simultaneously as well as in monolingual children. The children were equated on age, socio-economic status, nonverbal cognition, and on minority/majority language status (both languages have equal status), but differed in the amount of exposure they had received to each language spanning the continuum of bilingual exposure levels. A strong relationship was found between amount of exposure to a language and performance in that language. This relationship was different for receptive and expressive vocabulary. Children having been exposed to both languages equally scored comparably to monolingual children in receptive vocabulary, but greater exposure was required to match monolingual standards in expressive vocabulary. Contrary to many previous studies, the bilingual children were not found to exhibit a significant gap relative to monolingual children in receptive vocabulary. This was attributed to the favorable language-learning environment for French and English in Montreal and might also be related to the fact the two languages are fairly closely related. Children with early and late onset (before 6 months and after 20 months) of bilingual exposure who were equated on overall amount of exposure to each language did not differ significantly on any vocabulary measure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".