Pengaruh Komitmen, Kualitas Sumber Daya Manusia, Gaya Kepemimpinan Terhadap Kemampuan Penyusunan Anggaran pada Pemerintah Kota Manado
Bibliographic record
Abstract
The implementation of the local government can not be separated from the budget. In Regulation No. 13 of 2006, the draft budget work units contained in a document called the Draft Budget Unit (Rask). Rask standard includes expenditure analysis, a benchmark of the performance of standard costs as the principal instrument in the budget performance. The budget is important because it is used in the allocation of funds for the implementation of local government activities. In budgeting involvement of various work units (SKPD) in Manado City Government is indispensable. This is to improve the effectiveness and efficiency of governance and public service. This study aims to analyze Effect of Commitment, Quality of Human Resources, Leadership Style, to the ability of Local Government Budgeting in Manado. Sources of data in this study are primary data and secondary data in the form of a questionnaire. The population in this study is the employee on education (Department and Agency) Manado, and in this study the sampling method used was judgment sampling, the sample in this study is the Secretary, Head of Division (third tier). Data used in this study is mainly qualitative data were quantified by using multiple regression analysis. To test the quality of the data with validity and reliability. Besides testing the classical assumption of normality, multicollinearity and heteroscedasticity. The research proves that in partial Commitment, Quality of Human Resources does not affect the ability of Budgeting in Local Government Leadership Style Manado while variable positive effect on the ability of Local Government Budgeting in Manado.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".