Age, Experience and Language and Literacy Skills in English-Arabic Speaking Syrian Refugees
Bibliographic record
Abstract
Although age of acquisition (AoA) is frequently used when examining the endpoint of second language (L2) learning, it is rarely used to examine the initial phases of L2 acquisition. The present study provided a unique look at the role of AoA in early language and literacy acquisition in the L2 by a priori selecting two groups of Arabic-English speakers based on their ages, 6–8-year-olds (N = 43) and 9–13-year-olds (N = 53). These Syrian refugees were matched on English experience, having immigrated to Canada and having learned English for two years or less. Raw scores on language and literacy measures were compared across groups. The older group had higher scores on all first language (L1) variables. The groups did not differ on most L2 variables except for English word reading. Additionally, L1 and L2 variables were examined in relation to English word and pseudoword reading with different patterns of relations found for the two groups. For the younger group, phonological awareness and vocabulary were related to reading, while for the older group phonological awareness and morphological awareness were key predictors. These finding points to the unique relations among age, age of acquisition, L1 skills, and L2 language and literacy skills.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".