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Record W2900754682 · doi:10.1017/s0142716418000589

The development of a measure of root awareness to account for reading performance in the Arabic language: A development and validation study

2018· article· en· W2900754682 on OpenAlexaff
Sana Tibi, Jamie L. Tock, John R. Kirby

Bibliographic record

VenueApplied Psycholinguistics · 2018
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyFluencyReading (process)Confirmatory factor analysisArabicReading comprehensionStructural equation modelingPopulationPhonological awarenessRoot (linguistics)Variance (accounting)Cognitive psychologyNatural language processingDevelopmental psychologyLinguisticsMathematics educationComputer scienceStatisticsMathematics

Abstract

fetched live from OpenAlex

ABSTRACT Morphological awareness (MA) is an important predictor of reading outcomes in different languages. The consonantal root is a salient feature of Arabic lexical structure and critical to MA. The goals of this study were to (a) develop a measure of root awareness (RA) as one dimension of MA in Arabic, and (b) validate the RA measure by predicting reading outcomes in an Arabic population. A set of RA items was administered to 194 Arabic-speaking third-grade children. A one-factor model was specified using confirmatory factor analysis to examine the model fit of the RA measure. A structural equation model was then developed to examine the relation between the RA measure and important reading outcome measures including word reading, reading fluency, and reading comprehension. The results of these analyses indicated good model fit, and the RA measure accounted for a substantial portion of the variance in the outcomes. The establishment of the RA measure is an important preliminary step to efficiently assessing MA in Arabic and could serve as an integral tool for studying reading development.

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)0.002
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 score0.803
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.042
GPT teacher head0.363
Teacher spread0.322 · 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

Citations29
Published2018
Admission routes1
Has abstractyes

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