Bibliographic record
Abstract
The subject of the present research is the study of the relationship between earnings management (accrual-based and real) and auditor’s opinion. Alongside putting the control variables into consideration, this this paper studies the relationship between earnings management (accrual-based and real) and auditors’ opinion. The purpose of this research is to examine the effect of income smoothing and manipulation on the opinion of independent auditors. This research includes two independent variables i.e. earnings management (based on discretionary accruals) and real earnings management, one dependent variable i.e. auditor’s opinion, along with control variables. In the first main hypothesis the relation between real earnings management and auditor’s opinion is examined; and the second hypothesis involves the association between discretionary accrual-based earnings management and auditor’s opinion. In this research some 117 firms in the time period 2008-2013 are empirically investigated and studied using logistic regression method. In conclusion, the second and third hypotheses are rejected; however examination of the first and fourth hypotheses confirms their significant association with auditor’s opinion.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".