Policy Determination in E-Budgeting Implementation by the Government of DKI Jakarta - Indonesia
Bibliographic record
Abstract
This research discusses and analyzes the deep comprehensiveness of the process of determining and implementing the E-Budgeting Policy by the Government of the Jakarta Migrant Workers; Research methodology uses a qualitative approach. The research results reveal the effectiveness of the role of actors in the process of implementing E-Budgeting policies determined by several factors, namely: the level of understanding of budgeting procedures and mechanisms (APBD), activeness in providing input in the initial process (Musrenbag), and the ability to accommodate the interests of constituents into programs and concrete activities in the RKPD; the legislative role in implementing E-Budgeting can be mapped out by looking at the actions of the executive and executive, namely: first, the interaction of non-professional making models, is a form of meeting between the executive and the legislature to use the power and authority, use of the budget, process and use; secondly, the associative systemic pattern, is a model of executive and legislative relations that is influenced by political, economic, and social systems, so that the process of formulating the General Budget Policy (KUA) and the Budget Priority and Platform (PPAS) is not value-free because it is influenced by the interests and demands of various interest groups. If there are interests from groups that have more political resources and political power compared to other groups, then they are likely to influence budget decisions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".