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Record W2134603627 · doi:10.5539/jel.v3n3p45

The Influence of Diglossia on Different Types of Phonological Abilities in Arabic

2014· article· en· W2134603627 on OpenAlexvenueno aff
Ibrahim A. Asadi, Raphiq Ibrahim

Bibliographic record

VenueJournal of Education and Learning · 2014
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyStimulus (psychology)DiglossiaLinguisticsSpoken languageArabicPhonologyCognitive psychology

Abstract

fetched live from OpenAlex

The present study examined the impact of diglossia, a characteristic of the Arabic language, on the developmentof phonological abilities in the spoken and the literary language forms. Participants were 571 children from 10grade levels (1-7, 9, 11 and 12), which were recruited from 10 schools by taking into account two importantfactors: the accent factor (Bedouins, Druze and Arabs) and the geographical factor (south, Haifa, center andnorth). All participant were administered phonemic segmentation and phonemic deletion tasks, each comprisedof two types of stimulus: spoken and literary words. The results indicated an opposite effects of the stimuluswhere in the phonemic segmentation tasks, an advantage was found for the spoken stimulus over the literary andin the phonemic deletion task, the advantage was recorded in the literary stimulus. In addition, a significant maineffect of grade was found for both tasks. An interaction between grade and the type of stimulus was observedonly in the phonemic deletion task. These differences between the two tasks may suggest that they are processeddifferently via the auditory and the visual modality. In addition, our findings provide evidence concerns thedevelopmental capacity of phonemic awareness. The results, as a whole, support the notion that the effect oflexical distance on phonological awareness depends on modes of stimulus presentation.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.089

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 head0.012
GPT teacher head0.310
Teacher spread0.298 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

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