PELAYANAN PUBLIK KANTOR KELURAHAN KADIPIRO KECAMATAN BANJARSARI KOTA SURAKARTA
Bibliographic record
Abstract
Tujuan penelitian untuk mendeskripsikan dan menganalisis pelayanan publik Kantor Kelurahan Kadipiro. Metode penelitian menggunakan deskriptif kualitatif. Teknik pengumpulan data menggunakan wawancara, observasi, dan dokumentasi. Teknik analisis data menggunakan model analisis interaktif. Hasil penelitian menunjukkan bahwa: Pelayanan Publik berkaitan dengan Reliability (kehandalan) sudah dilaksanakan cukup baik. Pelayanan publik berkaitan dengan Tangibles (bukti langsung), dasarnya sudah dilaksanakan dengan baik. Pelayanan publik berkaitan dengan Responsiveness (daya tanggap), ditunjukkan kemampuan para staf dalam memberikan pelayanan sudah dilaksanakan dengan baik. Pelayanan publik berkaitan dengan Assurance (jaminan), pegawai memberikan pelayanan yang baik kepada masyarakat serta sopan dan santun. Pelayanan publik berkaitan dengan Emphaty (empati), ditunjukkan keseriusan dan ketulusan pegawai dalam melayani masyarakat, sikap tegas tapi penuh perhatian terhadap masyarakat. Kendala-kendala yang mempengaruhi pelayanan publik, antara lain: keterbatasan sumber daya manusia yaitu kurangnya jumlah pegawai, sarana dan prasarana yang masih kurang, komputer yang sering trobel dan lambat. Mengatasi kendala tersebut pihak Kelurahan Kadipiro memaksimalkan jam kerja, dan memanfaatkan anggota Linmas, menyarankan masyarakat untuk mencari informasi melalui media online, menyediakan computer cadangan. Kata kunci: reliability, tangible, responsiveness, Assurance, dan Emphaty
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.006 |
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".