Optimization Strategies for Government Asset Management in Jakarta Using Multi-Criteria Analysis
Bibliographic record
Abstract
The large number of government assets is often not managed properly, thus becoming a financial burden for the government, including the DKI Jakarta Provincial Government. On the other hand, the government has limitations in managing their assets. Therefore, cooperation in asset management and optimization is important to support the increase in local revenue. It is crucial to map government-owned assets so that they can be utilized in accordance with the highest and best use principle. This research uses data from 31 samples of Jakarta government asset data to map assets, focusing on land and buildings, using multiple criteria analysis. These criteria are compiled based on expert judgment and weighted using the analytical hierarchy process method. The results show that there are 6 assets in the high market category and high condition, 7 assets in the high market category but low condition, 8 assets in the low market category but high condition, and 9 assets in the low market category and low condition.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".