Modernising the Education Sector in Trinidad: A Political Perspective on Constraints to Policy Implementation
Bibliographic record
Abstract
(GJ ; ili, I, md f~,;, of<h, politician, implementation has always been a subject of political studies. Saetren (2005:559-582) observes, however, that research related to policy implementation in political studies as well as other areas of inquiry did not get much attention in the last decade. It was characterised as out of fashion or even dead. Reasons for this included, the unsolved protracted debate about whether or not a top-down or a bottom-up approach should be used; the selection bias towards cases involving implementation failure and growing doubts about the extreme difficulty in trying to segment the policy process into sequential stages (Saetren 2005). Recently, however, the debate has been renewed and this has fostered a much greater interest among political scholars as they try to develop a conceptual framework in an effort to determine why policy implementation in the public sector has been such an extremely difficult exercise.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".