The role of exposure on school-aged bilingual language abilities – it depends on what you measure
Bibliographic record
Abstract
Few studies have focused on bilingual language development in school-aged simultaneous bilingual children. The current study tackles this gap in the literature by examining the influence of language exposure on the development of both languages in school-aged simultaneous bilingual living in additive bilingual contexts. The language abilities of 19 school-aged simultaneous bilingual children were assessed using standardised language measures in French. Their amount of language exposure was also measured using a parent questionnaire. No correlation was found between language abilities in French and exposure to French. Results did, however, show that most of the children who received as little as 20% of their lifetime exposure to French, obtained scores within the average range on all of the measures. Additionally, children who were exposed to both of their languages on a daily basis were more likely to have better performances than their peers who used one of their languages less frequently. This effect was observed even though children in the former group received less exposure to their strongest language than children who were not exposed to both of their languages daily. Therefore, while exposure is important, other factors may have a positive influence on simultaneous bilinguals’ language proficiency levels.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".