Audit Lag Criteria Report as a Determination of the Reliability and Quality of Auditor's Report in Indonesia
Bibliographic record
Abstract
This paperaims to knowthe quality indicatorsof the financial statements which consist of profitability, solvency and reputation of Registered Public Accountant (KAP)to the audit lagwith company size as a moderation variable either partially or simultaneously in LQ45 companies. This research is a comparative causal research with ex post facto approach. Purposive sampling technique is used in this research and there are 18 samples collected by this technique from LQ45 in Indonesia Company Issueryear 2010-2016. The data analyzed research is 126. Data analysis technique used Moderated Regression Analysis (MRA) with the Application ofEviews Software. The study concluded thatstudy showed that solvency, reputation of the public accounting firm and company size had a significant effect on Audit Lag, while profitability had no significant effect on Audit Lag. The size of a company able to moderate the effect of independent variablesto the Audit Lag and not haveto moderate the effect of the profitability to the Audit Lag.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".