Should Forensic Accounting Be Included in Curricula of Accounting Departments: the Case of Saudi Arabia
Bibliographic record
Abstract
The objective of this research paper is to define the significance of forensic accounting and reflecting the need of including forensic accounting in the curricula of accounting departments in universities in the Kingdom of Saudi Arabia. The research followed the descriptive analytical approach. The research community consisted of a sample representative of faculty members in the accounting departments of universities in the Kingdom. Where (150) questionnaires were distributed and (126) questionnaires were collected, i.e., a response rate of (84%). The research employed the (SPSS) program to analyze and test its hypotheses, such as "There is no knowledge of the importance of forensic accounting among accounting department staff of Kingdom of Saudi Arabia universities". The research has reached several results, the most important of which are: all faculty members in accounting departments at universities in the Kingdom are aware of the significance of forensic accounting, and they agreed that it should be included in the accounting education curriculum. Finally, the research calls for numerous recommendations, the most important of which are: setting specific and clear plans for how to include forensic accounting in the accounting curricula in accounting departments in universities in the Kingdom, and organizing more (conferences, researches, training courses, workshops...) by accounting departments in universities in the Kingdom and other bodies that regulate the profession of accounting in the field of forensic accounting to further demonstrate its real significance.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".