The role of word reading and oral language skills in reading comprehension in Syrian refugee children
Bibliographic record
Abstract
Abstract Canada has resettled more than 57,000 Syrian refugees since 2015 (Government of Canada, 2017). However, little is known about refugee children’s language and literacy development. The present study evaluated Syrian refugee children’s performance on language and literacy measures in English and Arabic, and examined whether the simple view of reading model is applicable in both of their languages. Participants consisted of 115 Syrian refugee children 6–13 years of age. They received a battery of language and literacy measures including word reading, vocabulary, oral narratives, and reading comprehension in both English and Arabic. Compared to the normative samples, refugee children performed poorly on English standardized measures. They also demonstrated difficulties in Arabic, as more than half of the children were not able to read in the language. Despite the relatively low performance, there was evidence to support the simple view of reading model in both languages. In addition, oral language skills played a larger role in English reading comprehension in the older group than the younger group. This age-group comparison was not carried out in Arabic due to reduced sample size. Theoretical and practical implications of the findings are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 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".