Supervision mechanism and quality of the internal control system disclosure
Bibliographic record
Abstract
This study analyzes the effect of supervision activities on the quality of the internal control system.It ascertains how the board of commissioners direct the company in presenting information as required by the stakeholders.The presentation of adequate information on internal control is a part of good corporate governance.The study was conducted on a public company in Indonesia, and data of 119 companies were collected using content analysis.The items used in measuring the extent of disclosure were developed based on the 2013 COSO framework and directions in the circular letter of number 30 /SEOJK.04/2016.A regression test with the IBM SPSS 21 program was used to perform the data analysis.The analysis results showed empirical support for some characteristics of the board of commissioners, such as the size, accounting and financial literacy, and the number of meeting in a year on the quality of internal control system disclosure.This study provided a theoretical contribution by using supervision mechanisms to overcome asymmetric information.Practical contributions are also expected to be conveyed to the financial service authority regarding the independence proportion and gender diversity, that have not been generally performed.The value of this research is an instrument that measures the quality of the disclosure adjusting conditions in Indonesia.Design of measurement items based on rules is issued by the Financial Services Authority for public companies.
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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 | 0.019 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".