IMPLEMENTASI PEMERINTAHAN YANG BERSIH DALAM KERANGKA RENCANA AKSI DAERAH PEMBERANTASAN KORUPSI (RAD-PK) (Studi Di Kabupaten Pemalang)
Bibliographic record
Abstract
This research related to the implementation of good governance, free from corruption, collusion and nepotism. The approach used in this research is a descriptive qualitative approach. The Location of research conducted in the District of Pemalang. Based on the research results can presented that the District of Pemalang is committed and fully supports the government policy in eradicating corruption. District of Pemalang support to efforts to more information accelerate the eradication of corruption stated in the the Regional Action Plan to Accelerate the Eradication of Corruption (RAD-PK) in 2011 -2016 which refers to the Medium Term Development Plan (RPJM) District of Pemalang from 2011 to 2016 and the National Action Plan for Eradication of Corruption (RAN-PK) and the President of Republic of Indonesia Instruction No. 5 Year 2004 on Accelerating the eradication of corruption. RAD-PK 2011-2016 District of Pemalang is a document that contains an action program that aims to accelerate the eradication of corruption. RAD-PK as a program of action containing concrete measures that have been agreed by the stakeholders in the area, so it has been a commitment of local governments prevention efforts corruption through the development of programs and activities aimed at improving public services and the application of the principles of good governance. Keywords: governance, eradication, corruption
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".