EFEKTIVITAS PENGENDALIAN INTERNAL, ASIMETRI INFORMASI DAN IMPLEMENTASI GGG TERHADAP KECENDERUNGAN KECURANGAN AKUNTANSI PADA ORGANISASI PERANGKAT DAERAH KAB. INDRAGIRI HILIR
Bibliographic record
Abstract
Penelitian ini menguji secara empiris apakah efektivitas pengendalian internal, asimetri informasi dan implementasi konsep tata pemerintahan yang baik atau good government governance (3G) terhadap kecenderungan kecurangan akuntansi (Fraud accounting) pada Organisasi Perangkat Daerah (OPD) Kabupaten Indragiri Hilir Riau. Penelitian dilakukan pada 12 dinas yang ada di OPD Kabupaten Inhil. Teknik pengambilan sampel digunakan adalah sensus, sampel sebanyak 35 responden. Jenis data yang dipakai adalah data primer. Model analisis data digunakan adalah analisis regresi linear berganda, dengan pengujian kualitas data digunakan adalah uji validitas dan uji reabilitas.Hasil penelitian menunjukkan bahwa secara parsial variabel efektivitas pengendalian internal, asimeti informasi dan implementasi tata pemerintahan yang baik good governance governance berpengaruh baik terhadap kecenderungan kecurangan akuntansi pada OPD Kabupaten Indragiri Hilir provinsi Riau. Hasil uji koefisisen determinasi (R2) sebesar 76% sedangkan sisanya 24% dijelaskan variabel lain.
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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.010 |
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".