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Record W2032302085 · doi:10.1075/wll.16.1.04tib

Emergent writing of young children in the United Arab Emirates

2013· article· en· W2032302085 on OpenAlexaff
Sana Tibi, R. Malatesha Joshi, Lorraine McLeod

Bibliographic record

VenueWritten Language & Literacy · 2013
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsQueen's University
FundersUnited Arab Emirates University
KeywordsDiglossiaArabicNeuroscience of multilingualismCategorizationLiteracyPsychologyDevelopmental psychologyEmergent literacyWriting systemLongitudinal studyPedagogyMathematics educationGeographyLinguisticsMedicine

Abstract

fetched live from OpenAlex

We report results of writing samples of six Emirati children aged four to four and a half years collected at monthly intervals over an eight month period (the kindergarten academic year). Three teachers and six parents were interviewed to triangulate the data that were collected in the classrooms. The grounded theory method was used to code and categorize the data, which were then compared with the literature on emergent writing. Findings of this longitudinal study revealed that few opportunities are provided at home and in kindergarten for the development of young children’s emergent writing in Arabic and revealed other issues related to bilingualism and diglossia. Recommendations are provided for policy makers, teachers, and parents that would accelerate the development of young children’s Arabic literacy, particularly emergent writing skills, in the United Arab Emirates (UAE).

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)0.001
metaresearch head score (Gemma)0.004
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.299
Teacher spread0.288 · 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

Citations22
Published2013
Admission routes1
Has abstractyes

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