The role of intellectual capital as a mediation of relationship between audit committee and real earnings management
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 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.
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.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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