Assessing the Impact of Performance-Based Budgeting on Regional Development and Environmental Outcomes Mediated by Government Agency Accountability in Dairi District
Bibliographic record
Abstract
Provincial autonomy aims to stimulate and augment local capacities, fostering advanced, prosperous societies within autonomous regions.This goal requires the maximization of local potential and the encouragement of region-specific development, tailored to each Province's economic, geographical, and sociocultural attributes.This study employs a quantitative approach, involving 24 Provincial Apparatus Centers (DPOs), 15 districts, and 18 Community Health Centers within the Dairi government.A total of 121 respondents participated in the research.The study encompasses three variables: the dependent variable of provincial expansion, the independent variable of performance-based budgeting, and the mediating variable of government agency activity accountability.Performance-based budgeting scrutinizes the relationship between funding (input) and expected results (outputs), offering insights into the effectiveness and efficiency of activities.Budgets should be designed around the objectives achieved during the fiscal year, and the management and unit expenditures.The findings suggest that performance-based budgeting significantly and positively influences Provincial development in Dairi through the accountability process of government agency implementation, facilitating the evaluation of budget efficiency and effectiveness for Provincial development support programs.As a result, an enhanced level of community welfare, grounded in diversity, is realized.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".