Policy Implementation of Improving Education Quality of Primary Education Teachers in Laos and Indonesia
Bibliographic record
Abstract
The purpose of this research is to get deep meaning of policy implementation to improve quality of primary education teachers in Laos and Indonesia. Research locations are in the Ministry of Education in those both countries. This research used a qualitative approach with a multi-case study design. First, policy formulation consider the aspect of novelty and national education goals; second, policy dissemination of existing policies do after getting approval from parliament, as well as socialization is done to the department of education in each provinces and districts; third, policy implementation process is done by establishing a monitoring team to oversee that the policy can work well; fourth, monitoring and evaluation of the implementation is done periodically, at least every six months, and the results of the evaluation are reported to the Ministry; fifth, gaps in the policy implementation is because monitoring can not be run with maximum caused by geographical conditions and the weakness of the role of school supervisors; sixth, efforts to repair gaps in the implementation of policies to improve the quality primary school teachers is to make laws on the teacher, as well as to provide training to teachers and principals.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".