Spending Behavior Pemerintah Kabupaten/ Kota Se Eks Karesidenan
Bibliographic record
Abstract
Penelitian ini bertujuan untuk memberikan gambaran mengenai spending behavior pemerintah daerah Eks-Karesidenan Semarang dari tahun 2008-2012. Penelitian ini Dalam penelitian ini dilakukan meliputi daerah 6 kabupaten/kota di Eks-Karesidenan Semarang, meliputi Kabupaten Semarang, Kota Semarang, Kota Salatiga, Kabupaten Grobogan, Kabupaten Kendal dan Kabupaten Demak. Dengan menggunakan alat analisis varians belanja, analisis pertumbuhan belanja, analisis keserasian belanja (rasio belanja operasi terhadap total belanja dan rasio belanja modal terhadap total belanja), rasio efisiensi belanja, rasio belanja terhadap PDRB, rasio belanja pegawai terhadap total belanja daerah, rasio belanja modal terhadap jumlah penduduk, rasio belanja operasi terhadap jumlah penduduk, serta rasio belanja hibah dan bantuan sosial. Hasil analisis tersebut lalu dikomparasi dengan melakukan analisis time series dan cross section (data panel). Hasil dalam penelitian ini menunjukkan perilaku belanja pemerintah Kabupaten/Kota se Eks-Karesidenan Semarang terhadap kepentingan publik dalam hal pelayanan sudah baik. Tetapi perilaku belanja pemerintah Kabupaten/Kota se EksKaresidenan Semarang terhadap kepentingan publik dalam hal infrastruktur kurang baik. Penggunaan anggaran belanja Pemerintah Kabupaten/Kota se Eks-Karesidenan Semarang sudah efisien, pertumbuhan belanja di pemerintah Kabupaten/Kota se Eks-Karesidenan Semarang mengalami pergerakan fluktuatif. Pemerintah Kabupaten/Kota se EksKaresidenan Semarang mengalami penurunan produktivitas dan efektivitas.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".