Reading Comprehension in French L2/L3 Learners: Does Syntactic Awareness Matter?
Bibliographic record
Abstract
This study examines the contributions of syntactic awareness to reading comprehension, both within and across languages, in third-grade children learning French as a second (L2) or third language (L3). Participants were 72 non-francophone children enrolled in a Canadian French immersion program in which all academic instruction is in French. Children completed measures of reading comprehension, syntactic awareness, word reading, vocabulary, and reading-related control variables in both English and French. Regression analyses examining within-language relations revealed that French syntactic awareness made a significant unique contribution to French reading comprehension after controlling for nonverbal reasoning, language status (French as either L2 or L3), word reading, and vocabulary. Furthermore, French syntactic awareness contributed across languages to English reading comprehension, after accounting for English controls (word reading, vocabulary, syntactic awareness) in addition to nonverbal reasoning and language status. In sharp contrast, measures of English syntactic awareness made no unique contribution to reading comprehension in either English or French after the aforementioned controls. These findings add to theoretical models of reading comprehension by highlighting the importance of syntactic awareness in the language of instruction in supporting bilingual children’s reading comprehension.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".