The development of reading in English and Italian in bilingual children
Bibliographic record
Abstract
Canadian children (n = 81; 9–13 years) who spoke both English and Italian were administered phonological, reading, spelling, syntactic, and working memory tasks in both languages. There was a significant relationship between English and Italian across all phonological tasks. The relationship was less evident for syntactic skills and was generally absent for working memory measures. Analyses of phonological, syntactic, and memory processes based on levels of skill in English reading showed significantly better performance by skilled readers compared to less skilled readers; this was also true for the 11- to 13-year-olds compared to the 9- to 10-year-olds. Similar results were obtained as a function of levels of skill in Italian reading. On all Italian tasks, the bilingual children lagged behind monolingual children matched on age. However, less skilled and skilled bilingual Italian children had significantly higher scores than monolingual English–Canadian children (with comparable reading skills) on English tasks involving reading, spelling, syntactic awareness, and working memory. The results suggest that English–Italian interdependence is most clearly related to phonological processing, but it may influence other linguistic modules. In addition, exposure to a language with more predictable grapheme–phoneme correspondences, such as Italian, may enhance phonological skills in English.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".