Bibliographic record
Abstract
The government of Indonesia has long experienced an uneven pattern of budget realization. Our budget realization is characterized by small absorption in the first three-quarters and then piled up in the last quarter. An increase in spending at the end of the year eventually led to the quality of work on the national economy, which is not considered optimal. Through factor analysis, the researchers reviewed what factors are causing slow realization of the budget, especially for spending unit in the working area of KPPN Jakarta II. Several studies have been conducted to determine the problem, including Herriyanto (2012), BKF, LPEM-UI and IBRD (2012), Siswanto and Rahayu (2010), Miliasih (2012), Widjanarko (2013), and Fitriany (2015). Based on the factor analysis that has been conducted, it was found six factors that often slow down the realization of central government expenditure, especially for spending unit in working area of KPPN Jakarta II. The six factors include coordination, organizational culture, competence, technical constraints, administrative, and document. These six factors are derived from 27 indicators that were processed through the standard factor analysis, i.e. correlation between variables Kaiser Mayer Olkin (KMO), variables distribution and rotation of factors.
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".