Word reading in English and Arabic in children who are Syrian refugees
Bibliographic record
Abstract
Abstract Word reading is a fundamental skill in reading and one of the building blocks of reading comprehension. Theories have posited that for second language (L2) learners, word reading skills are related if the children have sufficient experience in the L2 and are literate in the first language (L1). The L1 and L2 reading, phonological awareness skills, and morphological awareness skills of Syrian refugee children who speak Arabic and English were measured. These children were recent immigrants with limited L2 skills and varying levels of L1 education that was often not commensurate with their ages. Within- and across-language skills were examined in 96 children, ages 6 to 13 years. Results showed that phonological awareness and morphological awareness were strong within-language variables related to reading. Additionally, Arabic phonological awareness and morphological processing were strongly related to English word reading. Commonality analyses for variables within constructs (e.g., phonological awareness, morphological awareness) but across languages (Arabic and English) in relation to English word reading showed that in addition to unique variance contributed by the variables, there was a high degree of overlapping variance.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".