A bibliometric analysis of research on forensic accounting from 2006 to 2024
Classification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.058 | 0.240 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it