Slicing the onion: reflections and projections on language education policy in the Caribbean
Bibliographic record
Abstract
Abstract The history of plantation slavery and European colonization in the Caribbean has left in its wake a rich and complex linguistic landscape, a colonial education structure, and a set of contradictory (creole/colonial) identities and language attitudes that make it fertile ground for a critical examination of language education policy development in the region. Using Jamaica, a former British colony as an illustrative case, this article takes a critical look at the historical and current framing and development of the multilayered “onion” that is language education policy in the Caribbean – a uniquely creole/colonial region with conflicting language ideologies. I examine the goals, actors, processes, and challenges and possibilities of LEP implementation (Nero, Shondel. 2018. Challenges of language education policy development and implementation in Creole-speaking contexts. In Jodi Crandall & Kathleen Bailey (eds.), Global perspectives on language education policies, 205–218. New York: Routledge and TIRF – The International Research Foundation), and also look ahead, recommending ways that we might craft viable 21st century education and LEP goals for the Caribbean and other former colonies around the world, given their colonial legacy and transnational present and future.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
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".