How does maternal education influence the linguistic environment supporting bilingual language development in child second language learners of English?
Bibliographic record
Abstract
Aims: In monolingual situations, mothers with higher levels of education are more likely to provide their children with enriched linguistic input. However, in bilingual situations, the relationship between maternal education and input is more complex because education may have occurred in one, but not both, of the languages. This study details this complexity by examining the relationship between maternal education and linguistic input in a group of children learning English as a second language (L2). Method: Participants were 89 immigrant/refugee children, living in Canada with diverse first languages (L1s). They completed a story-telling task in their L2 and parent reports provided a measure of L1 development. Parents also answered detailed questionnaires about family demographics and linguistic input within the family. Results: Regression modeling revealed that cumulative exposure to the L2 in school, maternal L2 fluency, relative quantity of L1/L2 use by the mother (input) and by the child (output) all influenced children’s L1 and L2 development. Relative quantity of input and maternal L2 fluency were, in turn, influenced by the amount of education mothers had completed and the language in which that education occurred. In instances where mothers were educated primarily in the L1, higher levels of education were associated with relatively more L1 input. For mothers who were educated predominantly in the L2, higher levels of education were associated with relatively more L2 input. Across both groups, mothers with higher levels of education had higher L2 fluency. Conclusion: This study revealed a complex relationship between the amount of education a mother has, the language in which that education occurred and the linguistic input she provides to her children. This complexity highlights that monolingual studies about input and environment may not offer a sufficiently nuanced perspective to account for the greater levels of variability found in bilingual situations. Originality: Previous research has offered valuable insights into the relationship between individual difference factors and bilingual acquisition. This study moves the field forward through the consideration of the interaction between factors in shaping bilingual acquisition.
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 | 0.001 | 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 | 0.000 |
| 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".