Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it