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Record W4412915541 · doi:10.1080/29984475.2025.2531361

Writing Development among Speakers of Oral and Non-standardized Languages in the Primary Years: A Systematic Review

2025· review· en· W4412915541 on OpenAlexaff
Shawna‐Kaye D. Tucker, Reshara Alviarez, Michelly Peixoto

Bibliographic record

VenueResearch Synthesis in Applied Linguistics · 2025
Typereview
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLinguisticsPsychologyComputer science

Abstract

fetched live from OpenAlex

A widely held belief in education, policy, and public discourse is that oral and non-standardized home languages hinder students’ literacy development – particularly in writing. This systematic review examines the characteristics of writing among children with oral home languages when writing in the language of literacy in the primary years, as well as the nature of writing interventions that have been designed to support this population across contexts. The search strategy spanned four educational databases, yielding 22 included studies. Findings indicate that while cross-linguistic influence from oral home languages is common, particularly in morphosyntax, it is neither the sole nor primary source of writing difficulties. Evidence from intervention studies point to the superior efficacy of explicit language awareness instruction, which contrasts home and school language features while valuing students’ full linguistic repertoires. Findings also emphasize the need for numerous opportunities for learners to push metalinguistic knowledge into productive use through written practice. However, methodological limitations, including small sample sizes, inconsistent reporting, a lack of methodological diversity underscore the need for more rigorous research. Further intervention research is needed that explores the efficacy of culturally responsive approaches, such as integrating oral traditions, in supporting writing development among learners.

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)0.012
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.189
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.060
GPT teacher head0.442
Teacher spread0.381 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes1
Has abstractyes

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