Firm Size, Profitability, Leverage as Determinants of Audit Report Lag: Evidence From Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the factors that influence Audit Report Lag in Indonesia. This factor is seen from the financial performance of the company size, profitability and corporate leverage. The research sample was 91 manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the period of 2015 and 2016. The total observation for 2 years amounted to 182. The method of data analysis is random effect models. The results showed that company size and profitability are variables that can shorten Audit Report Lag. Meanwhile, leverage has not empirically proven to have a significant effect. The findings implies that large companies have better information and technology systems compared to smaller companies so as to strengthen internal control and speed of presentation of financial statements. High profitability encourages companies to present financial reports on time so that the impact of ARL decline.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it