A bibliometric analysis of research on forensic accounting from 2006 to 2024
Bibliographic record
Abstract
This study examines the key trends, impacts, and contributions of research on forensic accounting from 2006 to 2024. Data were extracted from the Scopus database, and 109 research papers were filtered by applying the PRISMA framework, followed by a bibliometric analysis using the ‘biblioshiny’ tool of the R-studio package. The leading universities and institutions are Tshwane University of Technology and the University of Debrecen, in which research on forensic accounting is carried over time. Following the h-index and g-index criteria, the most impactful authors were the O.E. Akinbowale, A.D. Alves, C.T. Dang, T.T. Nguyen, Q. Fu, G. Judge, M.E. Lokanan, T. Ownes. Accounting Research Journal, Cogent Business and Management, Journal of Financial Crime, and Journal of Governance and Regulations are the most impactful sources of publication in forensic accounting. The results revealed that the UK, USA, Canada, and Germany are prominent countries in single-country publications, as well as multiple-country publications. The conceptual analysis disclosed subthemes as per the contemporary requirements of the field, such as forensic accounting techniques, fraud identification and risk assessment, and the role of certified public accountants in forensic accounting. This paper highlights the important sources, authors, and publications which will help research scholars summarise their literature review in the future and suggest upcoming areas of research in this field.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.188 | 0.260 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".