The effect of financial distress on earning management practices using classification shifting: The moderating effect of good corporate governance
Bibliographic record
Abstract
The existence of good corporate governance is expected to minimize the occurrence of earnings management practices when the company is in financial distress condition. This research aims to provide empirical evidence on the influence of financial distress on earnings management practices as well as the existence of good corporate governance projected by the proportion of independent commissioners and the proportion of audit committees in weakening the influence of financial distress on earnings management practices. The population of this study is property, real estate, and building construction sector companies listed on the Indonesia Stock Exchange for the period 2015-2019. Sampling techniques used are purposive sampling techniques and obtained samples as many as 185 samples. The earnings management tool used in this study was classification shifting. The data analysis techniques in this study used Eviews 10. The results of the analysis provide evidence that financial distress affects earnings management practices, while the proportion of independent commissioners is unable to moderate, and the audit committee strengthens the influence of financial distress on earnings management practices.
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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 | 0.004 | 0.022 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".