The role of intellectual capital as a mediation of relationship between audit committee and real earnings management
Bibliographic record
Abstract
This study aims to examine the role of human capital, which is part of intellectual capital, as a mediator in the relationship between audit committee expertise and the number of audit committee meetings with real earnings management. This research is a quantitative study. The data source used is data from manufacturing companies in Indonesia. The sample selection technique used purposive sampling. The analysis technique uses path analysis. The results showed that the expertise of the audit committee had a significant effect on human capital, while the number of audit meetings had no effect on human capital. The results of this study also state that audit committee expertise, number of audit committee meetings and human capital performance have no effect on real earnings management actions. Furthermore, there is empirical evidence that shows that human capital has a mediating effect on the relationship between audit committee expertise and the number of audit committee meetings with real earnings management. The role of human capital in the relationship between the expertise of the audit committee and the number of audit committee meetings becomes originality, so it is the main contribution of research. The limitation of this research is that it only uses human capital as a mediating variable.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".