IMPLEMENTASI NEW PUBLIC MANAGEMENT (NPM) DALAM PELAYANAN PUBLIK DI DINAS PENANAMAN MODAL DAN PELAYANAN TERPADU SATU PINTU KOTA MAKASSAR
Bibliographic record
Abstract
The purpose of this study was to analyze the implementation of the seven characteristics of NPM in theMakassar City Investment and one stop service (dPMPTsP). NPM is a reform in public managementas an effort to further improve the quality of services to the community, has seven characteristics in itsimplementation, namely: professional management in the public sector, has performance standards andperformance measures, emphasizes more on controlling output and outcomes, division of work units,the existence of competition in the public sector, applying the private sector management model to thepublic sector, emphasizing savings in the use of resources. Using a qualitative approach with the type ofevaluation research, namely examining the implementation of public service reform. Research informantsare structural officials of echelon II, III and IV, service operational staff, and the community. Research datawere collected through in-depth interviews, observations, and documentation related to policies. The resultsshow that of the seven characteristics of NPM, which have been implemented properly, namely professionalmanagement, the existence of performance standards and performance measures, the division of work units,the existence of competition in the public sector. The characteristics that still need to be taken seriously andaddressed are the emphasis on controlling output and outcomes, applying the private sector managementmodel to the public sector, emphasizing savings in the use of resources.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".