Revisiting the Storied Landscape of Language Policy Impact Over Time: A Case of Successful Educational Reform
Bibliographic record
Abstract
The many failures of large‐scale top‐down educational reforms are well documented in the reform literature. These failures are most evident when they are reviewed from the advantageous perspective of hindsight. What are less well documented are the extraordinarily interesting, centrally driven educational changes that have had important and lasting impacts over time, not only because they are rare, but also because they have often occurred outside the mainstream (North American) focus of the reform literature. This article provides a retrospective review of one such educational reform as unique as the tropical island country in which it occurred. Revisiting this storied landscape (Clandinin & Connelly, 1995) provides insight into the process and potential of a systemwide educational reform.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".