Language proficiency, reading comprehension and home literacy in bilingual children: the impact of context
Bibliographic record
Abstract
Numerous studies on reading comprehension with monolingual children have shown that oral language, such as vocabulary, is an important factor in predicting reading comprehension success. However, few studies have looked at the reading comprehension performance of bilinguals, and less is known about the contributors to its success, linguistic or otherwise. Based on previous research showing weaker oral language among bilingual children, the goals of the present study are to examine how bilinguals perform in reading comprehension, along with possible contributors such as oral language and home literacy practices, in comparison with their monolingual peers. Participants were 82 children in the third grade who completed standardized language measures assessing vocabulary, grammar, and reading comprehension and whose parents completed a home literacy questionnaire. Bilingual children’s reading comprehension was comparable to monolinguals despite having lower language, and bilingual parents reported reading rate was higher than that of the monolinguals. Moreover, the contributors to this success in reading comprehension were different for the bilingual group, with oral language and home literacy playing a role. Overall, this suggests bilinguals are unique from monolinguals in the manner in which they make use of the resources available to them, linguistic and otherwise, to achieve reading comprehension success.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".