Pengukuran Tingkat Kepuasan Masyarakat terhadap Pelaksanaan Kebijakan Pelayanan Pemerintah
Bibliographic record
Abstract
AbstrakMasalah pelayanan pemerintah yang dilakukan oleh aparatur pemerintah saat ini belum memenuhi harapan masyarakat. Hal ini dapat diketahui dari berbagai keluhan masyarakat yang disampaikan melalui media masa dan jaringan sosial, sehingga dapat memberikan dampak buruk terhadap kewibawaan pemerintah, yang menimbulkan ketidakpercayaan masyarakat. Tujuan tulisan ini adalah membahas konsep dan pemikiran yang berkaitan dengan kepuasan masyarakat, pelayanan publik, dan cara pengukurannya yang komprehensif. Hal yang paling penting dilakukan adalah survei kepuasan masyarakat terhadap penyelenggaraan pelayanan publik secara berkelanjutan sebagai dasar kemungkinan replikasi inovasi pelayanan publik. Perbaikan pelayanan publik yang paling mendasar adalah perbaikan sistem rekrutmen aparatur pelayan publik. AbstractProblems government service conducted by government officials at this time have not met the expectations of society. It can be seen from the public complaints submitted through the mass media and social networks, so as to adversely affect government authority, which give rise to public mistrust. The purpose of this paper is to discuss the concepts and ideas related to the satisfaction of the public, public service, and a comprehensive measurement method. The most important thing to do is to survey people's satisfaction with the implementation of public services in a sustainable manner as a basis for public service innovation possible replication. Improvement of public services is the most basic repairs apparatus public servant recruitment system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.015 |
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".