Model Reinventing Government Menuju Pemerintahan yang Baik (Good Government Governance)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
New Public Management has consequences for demands for bureaucratic reform and for a higher quality of public services for the community. The spirit of creating a results-oriented government has not only surfaced in developed countries, but also in developing countries, including Indonesia. New Public Management emphasizes the bureaucracy to be more professional in managing the country. This professionalism is shown, among other things, by the quality of managing the budget, improving performance management, and using bureaucratic performance measures as a standard measure of success. This study aims to find out how to describe and analyze governance and service excellence on government performance in the Bungo District Government. The sampling technique used was a saturated sample. This study used a questionnaire as a data collection method which was given to all civil servants (PNS) Echelon II, Echelon III and or Head of OPD in the work area of the Bungo Regency Government Office, namely 178 people. The results obtained from this study include the Bungo Regency Government Governance Level in the Good category, the Bungo Regency Government service excellent level in the good category and the Bungo Regency Government Performance level in the Good category. Governance and service excellent are important factors in improving the performance of the Bungo Regency Government, where the higher the level of governance and the Service Excellent level, the more it will improve the Bungo Regency Government's performance. Service Excellent cannot be a moderating variable between the influence of Governance on improving the performance of the Bungo Regency Government
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it