Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengidentifikasi besaran sumber-sumber pendapatan asli daerah, langkah-langkah yang ditempuh dalam peningkatan pendapatan asli daerah (PAD), mengeksplorasi hambatan-hambatan yang terjadi dalam optimalisasi sumber-sumber PAD, dan solusi strategis dalam optimalisasi PAD.Metode penelitiannya adalah kuantitatif dan kualitatif (mix method).Hasil penelitian menunjukkan bahwa besaran keempat sumber pendapatan asli daerah mengalami trend pendapatan yang fluktuatif selama tahun 2013-2017.Kontribusi keempat sumber pendapatan asli daerah terhadap pendapatan daerah dinilai masih rendah.Faktor-faktor penghambat optimalisasi pendapatan asli daerah Provinsi NTT terdiri dari faktor internal dan eksternal.Faktor internal meliputi budaya kelembagaan, administrasi (biaya operasional), teknologi informasi, kondisi geografis, kualitas dan kuantitas SDM.Sedangkan, faktor eksternal meliputi kepentingan politik tertentu, kondisi ekonomi masyarakat dan kesadaran masyarakat. Strategi-strategi yang telah diimplementasi dalam optimalisasi pendapatan asli daerah Provinsi NTT antara lain; strategi intensifikasi, ekstensifikasi dan peningkatan mutu pelayanan. Melalui hasil AHP, alternatif strategi yang dipilih adalah mengoptimalkan strategi inovasi.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".