PENGARUH PERATAAN LABA MELALUI MANIPULASI AKTIVITAS RIIL TERHADAP PERSISTENSI LABA
Bibliographic record
Abstract
This research aims to provide empirical evidence concerning the effect of income smoothing through real activities manipulation to the earning persistence. By using quarterly financial statement, this study also supposes to determine the timing of smoothing taken by the suspect firm. This study investigates three types of real activity manipulation: abnormal cash flow operation, abnormal discretionary expense, and abnormal production cost. Real earning manipulation is measured by summing the standardized of the three proxies. Companies that have been used as a sample in according to the purposive sampling criteria’s are consist of 63 firms on five years observations (2011-2015). From this number, samples included into income smoothing criteria based on Eckel Model are consist of 26 firms. The first hypothesis was tested with regression analisys and the second was tested with independent sample t-test. The first hypothesis test result showed that income smoothing via real earning manipulation negatively affect the earning persistence. But, the statistic test of the second hypothesis show that mean difference between earning persistence in the fourth fiscal quarter and in the other quarters was statistically insignificant. Thus, we can conclude that there is no difference between earning persistence in the fourth fiscal quarter and other quarters.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".