Compréhension écrite en français langue de scolarisation chez des enfants de langue d’origine arabe : contribution de facteurs cognitifs et linguistiques
Bibliographic record
Abstract
Reading comprehension, i.e., the ability to extract and construct meaning through the reader’s interaction and involvement with written language, results from the mobilization of linguistic and cognitive factors necessary, among other things, for the construction of the microstructure (local meaning) and the macrostructure (global meaning) in reading ( Kintsch, 1998 ). This ability is a prerequisite for learners’ academic success ( Bialystok, 2001 ). However, heritage language children, i.e., those from a migrant background who speak or at least understand their language of origin, which is different from the majority language and from the language of instruction ( Valdés, 2001 ), often have a disadvantage in reading comprehension compared to native speakers of the language of instruction. This is generally attributed to linguistic or cognitive differences ( Lervåg and Aukrust, 2010 ). Our research investigated the relationship between reading comprehension, linguistic factors (oral comprehension and metasyntactic ability) and cognitive factors (working memory and phonological memory) in 62 heritage language children of Arab origins, attending French schools in Montreal. The results highlighted significant differences in the contribution of the targeted variables in the construction of the microstructure and the macrostructure in reading.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".