Government policy in the field of audit and financial audit infrastructure on the smoothness of the supply chain and its implications on financial management compliance
Bibliographic record
Abstract
The aim of this research is to analyze the influence of government policies in the field of audit and financial audit infrastructure in improving the smoothness of the supply chain and its implications for financial management compliance in regional apparatus organizations (OPD) within the Serang City Government, Banten Province, Indonesia. The sample in this study was 225 respondents consisting of 19 from Regional Apparatus Organizations (OPD) within the Serang City Government. Sampling technique using technique purposive sampling. Data collected through questionnaires was then analyzed using SEM-PLS. The results of research and data analysis show that: Government Policy in the Audit Sector directly has a positive and significant effect on the smoothness of the Supply Chain; Financial Audit Infrastructure directly has a positive and significant effect on the smooth running of the Supply Chain; Government Policy in the Audit Sector directly has a positive and significant effect on Financial Management Compliance; The government's Financial Audit Infrastructure directly has a positive and significant effect on Financial Management Compliance; The smoothness of the Supply Chain directly has a positive and significant effect on Financial Management Compliance in Regional Apparatus Organizations (OPD) within the Serang City Government, Banten Province, Indonesia. The smoothness of the Supply Chain is able to partially mediate Government Policy in the Field of Audit and Financial Audit Infrastructure on Management Compliance in Regional Apparatus Organizations (OPD) within the Serang City Government, Banten Province, Indonesia.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".