Decentralization of Education in Indonesia—A Study on Education Development Gaps in the Provincial Areas
Bibliographic record
Abstract
Decentralization is acknowledged as the handover of government from central government to local government, including giving broader authority to local governments to manage education. This study aims to discovering education development gap between regions in Indonesia as a result of decentralization. This research method uses descriptive analysis that is supported by a combination of time series data and cross section data. Time series data used is the year 2014-2015, and the cross section data of 34 provinces in Indonesia. Gaps were revealed on the resources (including budgets, school facilities, and teachers), school participation, and the population that is illiterate in the area. The results showed that the persistence of the education development gap between regions. Gaps school facilities and number of teachers between regions still exists. The number of existing school facilities in some areas did not meet to accommodate all students. The ratio of the number of schools with teachers is still not meet. School participation rates in the provincial area still tend to be low, especially for the age group 16-18 and 19-24 years. There is gap between regions to reduce the population is illiterate, there are areas have a number of illiterates is still high despite the provincial area having income that is quite large. The study also found that, overall, the decentralization of education in Indonesia increase in the number of school participation and decrease the number of illiterate population in the provincial area.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".