Pengaruh Dana Bagi Hasil Provinsi Dan Bantuan Keuangan Pemerintah Provinsi Terhadap Produk Domestik Regional Bruto Kabupaten Simalungun
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menganalisis pengaruh parsial DBH Propinsi dan BKP Propinsi terhadap PDRB Kabupaten Simalungun dan pengaruh simultan DBH Propinsi dan BKP Propinsi terhadap PDRB Kabupaten Simalungun. Berdasarkan jenis masalah yang diteliti, pendekatan yang digunakan adalah deskriptif kuantitatif dan verifikatif kuantitatif. Hasil penelitian menunjukkan bahwa Dana Bagi Hasil (DBH) Provinsi dan Bantuan Keuangan (BKP) Provinsi secara simultan berpengaruh signifikan terhadap PDRB Kabupaten Simalungun. Dari hasil uji F (simultan) yang dilakukan, diperoleh Fhitung sebesar 14,358 dimana Ftabel adalah 4,74. Dengan demikian Fhitung (14,358) > Ftabel (4,74), maka DBH dan BKP Provinsi secara simultan berpengaruh signifikan terhadap PDRB Kabupaten Simalungun. Secara parsial Dana Bagi Hasil (DBH) Provinsi berpengaruh signifikan terhadap Pengembangan Wilayah Kabupaten Simalungun. Nilai signifikansi DBH Provinsi diperoleh sebesar 0,03 lebih kecil dari signifikansi Alpha 0,05 yang menunjukkan bahwa DBH Provinsi berpengaruh signifikan terhadap PDRB. Sementara nilai signifikansi BKP Provinsi diperoleh sebesar 0,058 dimana nilai ini lebih besar dari nilai Alpha 0,05, dengan demikian BKP Provinsi secara parsial tidak berpengaruh signifikan terhadap PDRB Kabupaten Simalungun
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".