Determinants of enterprise risk management disclosures: Evidence from insurance industry
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
The aim of this study is to determine if there is a relationship between the number of commissioners on the committee, ownership concentration, the Risk Management Committee, business size, and leverage on enterprise risk management. Take, for example, insurance companies listed on the Indonesia Stock Exchange. Purposeful sampling was used in the sampling process. Supplementary data was obtained from the website of the Indonesia Stock Exchange. Panel data regression analysis was used as the research method. Although business size had an effect on enterprise risk management transparency, board size, stake concentration, the risk management committee, and leverage had little effect. By integrating the variables Board of Commissioners Size and Ownership Concentration, as well as employing dynamic equation modeling to examine the above relationships, which have been overlooked in previous analyses, and analyzing more recent evidence from a developed world perspective, this study contributes to the management accounting literature and organization theory. The findings would be useful to Indonesian practitioners, especially those in management positions in insurance companies and financial institutions.
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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 | 0.001 | 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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