The role of budget decision making in mediating trust, social ties, cultural context and local government performance
Bibliographic record
Abstract
Indonesian Corruption Watch (ICW) research reveals that corruption in Indonesia is mostly carried out by civil servants. An audit report from the Indonesian Supreme Audit Institution also finds local government internal control systems' weaknesses, especially controlling budget execution. This phenomenon is inseparable from the budget actors' decision-making process when preparing a budget. Budget decision making is a crucial process. Budget actors use their preference to choose among priorities and put the programs and activities which they have chosen into the budget. Social exchange such as trust and social ties can restrain budget actor distortion when making a budget decision and improve local government performance. Cultural context, in this research, refers to high context, affects individual behavior, and may influence the budget decision-making process. The result shows that decision making mediates trust and context culture to local government performance. Trust also proven can improve local government performance. Social ties, on the other side, do not have an influence both on decision making and local government performance. When trust has been intertwined among members, social ties no longer have a role in social exchange. Trust is the strongest form of social relation. Trust in this research is also formed because of the figure. A figure holds an important role in high context culture. This research pointed out that decision making by considering social values can make the decision-making process better and even improve local government performance.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".