Factors influencing the information quality of local government financial statement and financial accountability
Bibliographic record
Abstract
The purpose of this paper is to study the effect of internal control system and human resource competence on information quality of local government financial statement and financial accountability. The method of collecting data is questionnaire which is distributed among 161 out of 303 population of employees of the Agency (Dinas) in the Government of South Sumatra Province, Indonesia. The collected data is processed by using SPSS 20.00 with t-test and Path Analysis. The result shows that internal control system and human resource competence positively influenced on the information quality of local government financial statement. Internal control system and human resource competence also influence positively on financial accountability both directly and indirectly mediated by the information quality of local government financial statement. Moreover, the information quality of local government financial statement directly and positively influence the financial accountability. The re-sults of this study can be beneficial for the government as input and material considerations in determining policies specifically related to improve the quality of information on government financial statements and financial accountability.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".