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Record W3048532954 · doi:10.1017/s014271642000034x

Word reading in English and Arabic in children who are Syrian refugees

2020· article· en· W3048532954 on OpenAlexaff
Alexandra Gottardo, Norah Amin, Asma Amin, Redab Al‐Janaideh, Xi Chen, Johanne Paradis

Bibliographic record

VenueApplied Psycholinguistics · 2020
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of AlbertaUniversity of TorontoWilfrid Laurier University
Fundersnot available
KeywordsPsychologyPhonological awarenessReading (process)LinguisticsReading comprehensionSemitic languagesWord recognitionArabicLearning to readPhonologyComprehensionFirst languageMetalinguisticsVocabulary development

Abstract

fetched live from OpenAlex

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.288
Teacher spread0.273 · 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.

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

Citations15
Published2020
Admission routes1
Has abstractyes

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