ACHIEVEMENT OF MINIMUM EDUCATION SERVICE STANDARDS FOR INDONESIA DURING THE COVID 19 TRANSITION PERIOD
Bibliographic record
Abstract
The emergence of the Covid 19 virus in 2019 has presented a big challenge for local governments in carrying out compulsory education affairs through the achievement of the Minimum Service Standards (SPM) in 2020. So, in 2020, the government made several policies in handling Covid 19, one of which is refocusing the budget from a predetermined budget. The purpose of this study was to see the achievement of the Education (SPM) in 2020 during the Covid 19 transition in the South Tangerang City Government, through a qualitative case study method with the CIPP approach model. The results showed that the South Tangerang City Government had succeeded in achieving two targets for Minimum Service Standards (SPM) for Education in 2020 (the indicators for Basic Education and Equality Education), by implementing performance effectiveness and changing the budget structure in the third quarter after budget refocusing. The achievement indicators for Early Childhood Education (PAUD) were not on target because the people of South Tangerang choose PAUD schools in Jakarta as their place of work. The conclusion from the research was that the South Tangerang City Government has succeeded in carrying out work effectiveness during the COVID-19 transition by refocusing the budget through the effectiveness of employee performance and replanning the 2020 work program
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".