Bibliographic record
Abstract
Purpose This paper aims to review the relevant forensic accounting research (FAR) around the world and suggests avenues for future research in forensic accounting. Design/methodology/approach The study used the thematic and systematic literature review methodology to analyse the existing literature in FAR. Findings The major thematic areas in the literature are fraud motivation, fraud consequences, fraud detection using forensic accounting techniques, forensic accounting theory, forensic accounting skills, forensic accounting education and forensic accounting jobs. The quantity of FAR is relatively small compared to the quantity of research in other accounting specializations. FAR is well developed in the USA and Canada and is less developed in Europe, Oceania and Asia. There is high interest in FAR in African countries. There is a relatively low global interest in internet information about “forensic accounting research” compared to global interest in other forensic accounting topics. Areas for future research include the role of the environment, digitalization, religiosity and sustainable development in forensic accounting. Practical implications FAR around the world is lopsided, as some regions have more advanced FAR compared to other regions. There is a need for even development of FAR across all regions and a need to publicize the outputs of FAR to a larger audience to increase people’s interest in forensic accounting. Originality/value The study extends the literature by presenting a rigorous thematic and systematic review of the existing literature. It highlights the depth of FAR, the major thematic areas, the benefits of FAR to society and the geographical reach of existing FAR.
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 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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".