An Analysis of Minimum Service Standards (MSS) in Basic Education: A Case Study at Magelang Municipality, Central Java, Indonesia
Bibliographic record
Abstract
The study aims at analyzing the achievement of Minimum Service Standards (MSS) in Basic Education through a case study at Magelang Municipality. The findings shall be used as a starting point to predict the needs to meet MMS by 2015 and to provide strategies for achievement. Both primary and secondary data were used in the study investigating the gap between the real achievements and the standards as set out in MMS in terms of the number of schools and classrooms, support facilities, teachers, education staff, including books and teaching media. A little bit of non-personnel budgeting was also observed to be matched with the standards as set out by Education Ministry No. 69/2009. It turned out that classrooms and teachers outnumbered in Magelang with reference to the distribution of students and teachers according to national MMS even seemingly a waste of money. Teachers were abundant 2010 and would remain as such in 2015. Classrooms for Elementary Schools would be sufficient up to 2015, and six additional classrooms would be needed for Junior High Schools. However teachers’ qualifications were far from fulfilling MMS including the school principals. There lacked support facilities including books and teaching media. Meanwhile non-personnel budgets were low including allocation of funds for school supplies. Therefore, it is high time that City Government prioritized 9-year-obligatory education by redistribution of students and teachers, improvement of teachers’ qualifications, upgrade of school facilities in accordance with MMS and improved efficiency of school budgets.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".