The development of language and reading skills in the second and third languages of multilingual children in French Immersion
Bibliographic record
Abstract
The relationship between first language (L1) typology, defined as the classification of languages according to their structural characteristics (e.g. phonological systems and writing systems), and the development of second (L2) and third (L3) language skills and literacy proficiency in multilingual children was investigated in this study. The sample included 90 children in Grade 4: tested once at the beginning of Grade 4 (T1) and again at the end of Grade 4 (T2). The children belonged to one of three language groups: English monolinguals, multilinguals who were literate in an alphabetic L1, and multilinguals who were literate in a logographic/syllabary L1. The study examined the extent to which the development of L2 and L3 literacy skills varied primarily as a function of orthographic similarities with the L1. Results revealed that multilingual children who were literate in an alphabetic L1 showed advantages in L2 and L3 reading comprehension. However, there were no differences on tasks that measured word reading and pseudoword reading. A more accurate picture of what facilitates L2 and L3 reading development is enhanced when differences in L2 and L3 proficiency were considered as well.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".