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Record W2591908855 · doi:10.1017/s0142716417000029

Morphological awareness: Construct and predictive validity in Arabic

2017· article· en· W2591908855 on OpenAlexaff
Sana Tibi, John R. Kirby

Bibliographic record

VenueApplied Psycholinguistics · 2017
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsQueen's University
FundersAbu Dhabi Education Council
KeywordsPseudowordPsychologyConstruct validityPredictive validityReading (process)SentenceConstruct (python library)Incremental validityTest validityVariance (accounting)ValidityAbu dhabiDevelopmental psychologyLinguisticsPsychometricsNatural language processingCognitionComputer science

Abstract

fetched live from OpenAlex

ABSTRACT The purposes of this study were to examine the dimensions underlying morphological awareness (MA) in Arabic (construct validity) and to determine how well MA predicted reading (predictive validity). Ten MA tasks varying in key dimensions (oral vs. written, single word vs. sentence contexts, and standard vs. local dialect) and two reading tasks (real word and pseudoword reading) were administered to 102 Arabic-speaking Grade 3 children in Abu-Dhabi. Factor analysis of the MA tasks yielded one predominant factor, supporting the construct validity of MA in Arabic. Closer inspection revealed that this factor had two subcomponents, oral and written. Hierarchical regression analyses, controlling for age and gender, indicated that both the one- and the two-factor solutions accounted for 48% of the variance in word reading, and 40% of the variance in pseudoword reading, supporting the predictive validity of MA. Implications for future research, assessment, and instruction 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.368
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), 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

Citations63
Published2017
Admission routes1
Has abstractyes

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