Earnings quality before and after the implementation of PSAK 69
Bibliographic record
Abstract
PSAK 69 Agriculture regulates the accounting treatment of agricultural activities in Indonesia. The measurement of biological assets is the most important part of the arrangement of PSAK 69. PSAK 69 deals with biological assets measured at fair value less costs to sell at the beginning and end of the reporting period. Characteristics of growing biological assets will have an impact on the growth in fair value of assets, so there will be differences in fair value at the beginning and end of the financial reporting period. The difference in fair value of biological assets, whether realized or not, is recognized as gain in the current period. This will have an impact on the quality of the company's earnings. This study aims to examine differences in earnings quality before and after the implementation of PSAK 69 in agricultural sector companies listed on the Indonesia Stock Exchange. The research was conducted on 14 agricultural companies listed on the Indonesia Stock Exchange in the 2016-2019 observation period. Earnings quality is measured by the earnings response coefficient. Earnings response coefficients are estimated using the firm specific coefficient model (FSCM) and pooled cross-sectional regression model (CSRM) methods. This study measures the quality of earnings before and after the application of PSAK 69. The quality of earnings before and after the application of PSAK 69 is tested by a paired two-sample t-test. The results of this study found no difference in earnings quality before and after the application of PSAK 69.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".