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Record W4405653019 · doi:10.1002/rrq.601

Writing in Creole Contexts: A Study of Jamaican Primary School Students

2024· article· en· W4405653019 on OpenAlexaff
Shawna‐Kaye D. Tucker, Hamish Chalmers, Victoria A. Murphy

Bibliographic record

VenueReading Research Quarterly · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of Toronto
FundersClarendon FundUniversity of Oxford
KeywordsCreole languagePsychologyPrimary educationPedagogyMathematics educationLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Abstract Creole‐speaking contexts are significantly underrepresented in language and literacy research yet present a unique context for understanding the nature of language and literacy development among numerous learners in the Global South. In the Caribbean in particular, the poor writing outcomes of Creole speakers across all levels of education has been a subject of lament for educators and policymakers for several years. Given the significant differences between the home and school languages, particularly in the areas of grammar and phonology, as well as the importance of these skills in writing, it is worth exploring the nature of writing challenges among Creole dominant learners in the Caribbean. This paper outlines an empirical study exploring the nature of writing challenges experienced by Creole dominant primary school learners in the Jamaican context. As part of a larger mixed‐methods study, students completed a narrative writing task which was assessed with reference to an analytic rubric. Findings showed that beyond grammar, which has largely been the focus of extant literature, Creole dominant learners experienced significant challenges in lower‐order transcription skills and higher‐order oral language skills at the word, sentence, and text levels. Findings are discussed in line with the not‐so‐simple view of writing and recommendations for supporting the literacy development of Creole‐speaking learners in the Caribbean are outlined.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.130
GPT teacher head0.588
Teacher spread0.458 · 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 designQualitative
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

Citations3
Published2024
Admission routes1
Has abstractyes

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