Quality Assurance Accreditation Standards and the Forensic Accounting Profession in Nigeria
Bibliographic record
Abstract
This study examined the level of compliance of the forensic accounting profession in Nigeria with the Quality Assurance Accreditation Standards (QAAS), and the present state of the profession in the country, using both primary and secondary data. Descriptive and inferential statistics were used to analyze the data obtained from 161 respondents. A significant difference was found between the current state of the forensic accounting profession in Nigeria and the requirements of the QAAS. This study found that the profession lacks statutory regulatory institutions which enable legal frameworks in Nigeria. It was also found that the contribution of Nigerian universities to the advancement of forensic accounting is not significant due to insufficiently trained university lecturers and lack of facilities. This led to an acute shortage of qualified professionals in practice and a low standard of service delivery in the field. Many of the existing practitioners lack the requisite skills to function effectively. We concluded that the profession in Nigeria has not provided the expected impact on the fight against economic and financial crimes, and without QAAS in place the profession would remain powerless against the economic and financial crimes in the country. We recommend that the Nigerian Federal Government should create forensic regulatory institutions to ensure that the forensic accounting profession in Nigeria is focused on best practices and complies with the QAAS requirements.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".