Simultaneous bilingual language acquisition: The role of parental input on receptive vocabulary development
Bibliographic record
Abstract
Parents often turn to educators and healthcare professionals for advice on how to best support their child's language development. These professionals frequently suggest implementing the 'one-parent-one-language' approach to ensure consistent exposure to both languages. The goal of this study was to understand how language exposure influences the receptive vocabulary development of simultaneous bilingual children. To this end, we targeted nine German-French children growing up in bilingual families. Their exposure to each language within and outside the home was measured, as were their receptive vocabulary abilities in German and French. The results indicate that children are receiving imbalanced exposure to each language. This imbalance is leading to a slowed development of the receptive vocabulary in the minority language, while the majority language is keeping pace with monolingual peers. The one-parent-one-language approach does not appear to support the development of both of the child's languages in the context described in the present study. Bilingual families may need to consider other options for supporting the bilingual language development of their children. As professionals, we need to provide parents with advice that is based on available data and that is flexible with regards to the current and future needs of the child and his family.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".