The effect of financial distress on earning management practices using classification shifting: The moderating effect of good corporate governance
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 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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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