The Caribbean Certificate of Secondary Level Competence in Context: Local Adaptations and Global Lessons
Bibliographic record
Abstract
This chapter discusses the Caribbean Certificate of Secondary Level Competence (CCSLC) programme, its justification in the context of the Caribbean region, and how it compares to the Curriculum for similar grades in other contexts, such as Australia, Canada, the United Kingdom, and the United States of America. The CCSLC programme was developed by the Caribbean Examinations Council (CXC) in response to a call by the Caribbean Common Market (CARICOM), heads of governments for a foundational programme to better prepare students at the lower levels of secondary school (Grades 7–9) for them to perform better at higher Grades and the Caribbean Secondary Education Certificate (CSEC) examination. CCSLC is adapted across several Caribbean countries. Guyana embraced the programme in 2024 and continues to prepare teachers and school administrators as the country advances to full implementation. Ninety-two per cent of the 73 schools trained thus far have implemented the programme. Implementation challenges include the lack of training, the need for retraining, the scarcity of specialist teachers, and limited material resources. This chapter also highlights teachers’ perspectives on adapting to Guyana’s context and finds that there is widespread buy-in for the programme’s implementation due to its potential to improve students’ performance. To retain teachers’ enthusiasm for supporting the programme and ensuring effective delivery and intended educational outcomes, an expanded support system is recommended.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".