Impact of Forensic Accounting Domains on Financial Corruption in Lebanon – An Empirical Study
Bibliographic record
Abstract
Forensic accounting was developed after the widespread corruption in the world of business today. It is now considered a fundamental branch of accounting since it revolves around disputes and issues in the law that require accounting and legal knowledge and practices to be resolved. This study examines the impact of two forensic accounting domains – the expert witness and litigation support – on financial corruption in Lebanon. The study adopts the analytical descriptive approach utilizing an empirical study. A well-structured five-point Likert style questionnaire was devised as the study tool and was distributed among a sample of 323 of the total population that consists of all certified public accountants (CPAs) in Lebanon, tax controllers, senior tax controllers, and heads of the departments and divisions in the Lebanese Ministry of Finance, and auditors at the Lebanese Audit Bureau. The study reached important findings, mainly that there is an impact of the expert witness, which is one of the forensic Accounting domains, on curbing financial corruption in Lebanon, and that there an impact of the litigation support, which is one of the forensic Accounting domains, on curbing financial corruption in Lebanon.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".