Writing in Creole Contexts: A Study of Jamaican Primary School Students
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".