The awareness of judicial accounting techniques towards the expectations of the external auditor in detecting fraud and its impact on the performance
Bibliographic record
Abstract
Judicial accounting outputs are reports that guide judges in conflicting parties over financial litigation, supporting judicial cases, and settling and resolving disputes. As a discipline, judicial accounting applies the science and knowledge of accounting, such as finance, taxation and auditing in the form in which the judicial accountant can provide his expert opinion, through the availability of a set of techniques in the field of fraud investigation and support of lawsuits, to investigate the allegations alleged by the relevant parties, especially those allegations related to the existence of fraud, as the objective of the judicial accountant will depend on the purpose of his assignment, including investigating the presence of fraud. The external auditor's reliance on the sampling method when checking financial disclosure and his lack of responsibility for detecting fraud highlights the importance of judicial accounting in detecting fraud by employing a set of techniques, to assist him in detecting fraud. The achievement of its objectives by the judicial accountant also requires set of characteristics such as education, training, diverse experience in the field of accounting, auditing and law, oral and written communication skills, and the ability to work in a team environment. Judicial accounting is based on a range of techniques, for instance, including Benford's law, computer-based audit tools, data mining and analysis to show the role of judicial accounting techniques in the judicial accountant’s awareness of his duties towards the external auditor in detecting fraud and its impact on developing his performance.
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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 | 0.019 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".