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Record W3044078966 · doi:10.1017/s0142716420000284

The role of word reading and oral language skills in reading comprehension in Syrian refugee children

2020· article· en· W3044078966 on OpenAlexaffabout
Redab Al‐Janaideh, Alexandra Gottardo, Sana Tibi, Johanne Paradis, Xi Chen

Bibliographic record

VenueApplied Psycholinguistics · 2020
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of AlbertaWilfrid Laurier UniversityUniversity of Toronto
Fundersnot available
KeywordsRefugeePsychologyReading comprehensionReading (process)Semitic languagesLiteracyVocabularyComprehensionArabicNarrativeLinguisticsPedagogyPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.294
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2020
Admission routes2
Has abstractyes

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