ANALISIS KINERJA KEUANGAN PEMERINTAH DAERAH KABUPATEN KAUR
Bibliographic record
Abstract
One positive impact of the implementation of regional autonomy is the expansion of provincial and district / city that almost occurred throughout Indonesia. One area is the result of the expansion area Kaur regency in Bengkulu Province. As a new district that grows future regional autonomy, Kaur District has the authority to manage their own regions. The purpose of this study was to determine the Financial Performance of the District Government Kaur. Data collection method used is documentation. While the method of analysis using quantitative methods using financial ratios. Financial Performance of the District Government Kaur years 2001-2014 when viewed from the Regional Financial Independence Ratio is relatively low once (an average of 2.44% per year). Effectiveness Ratio PAD is known that the effectiveness of Kaur regency in 2011, 2013 and 2014 runs Ineffective indicated by the value ratio between 75% -89%, but in 2012 went very effective with a ratio reached 107.3%. Activity Ratio of the ratio of Operating Expenditure quite well that the value ratio between 50% -100% or the average value of 76.7% per year, while the ratio of Capital Expenditure classified as not good because it has value ratio is less than 50% or the average value -rata year by 23.2% per year). Growth in revenue (PAD) Kaur District has increased from year to year, but the growth was relatively moderate growth with an annual average value of 45.22% per year. Key Words: Financial performance, Ratio of Regional Financial Independence, Effectiveness Ratio, Activity Ratio, Growth Ratio, Kabupaten Kaur.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".