PENGARUH PENERAPAN STANDAR AKUNTANSI PEMERINTAH, IMPLEMENTASI SISTEM INFORMASI MANAJEMEN DAERAH, DAN SISTEM PENGENDALIAN INTERNAL PEMERINTAH TERHADAP KUALITAS LAPORAN KEUANGAN PEMERINTAH DAERAH KABUPATEN SELUMA
Bibliographic record
Abstract
Effect of application of government accounting standards, implementation of regional management information systems, and government internal control systems on the quality of financial statements at the regional government of seluma district. This research used a quantitative approach, using primary data through questionnaires. Respondents of this research were 26 OPD of part of a financial manager a the regonal governmnt of seluma district. The Variables in thiss researcch is the government accounting standards, implementation of regional management information systems, and govrnment internal contcl systems as independent variables, as well the qualty of local governmnt financiial statements as the dependent variable. The data were analyzed with multiple regression method. The results of hypothesis shown that the government accounting standards, implementation of regional management information systems, and governent internal contral systems give the impact add it was positive to the qality of financial statemnts at the ragional govermment of seluma district.Key words: Management Information Systems, And Government internal Control Systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.001 |
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".