A bibliometric analysis of research on forensic accounting from 2006 to 2024
Bibliographic record
Abstract
The purpose of the present study is to bring out diverse attributes of the literature review in the field of forensic accounting. The data have been extracted from the SCOPUS and 109 research papers filtered by applying PRISMA framework followed by bibliometric-analysis. The ‘biblioshiny’ tool of the R-studio package is used for analysis purposes. The output of the said package presented in tables and graphs. The study reveals that empirical research on the proposed topic is widely conducted in the western nations. The TC and NP of the developed nation is much higher than any other country. The SCP and MCP is higher, in case of source of the journals, of developed nation. The most relevant author, research paper, affiliations, and countries are also from the developed nation only. It opens an opportunity for the developing nation to undertake research in this field. The conceptual analysis of the study reveals sub-themes as per the contemporary requirements of the filed viz., forensic accounting techniques, fraud identification and risk assessment, role of certified public accountants in forensic accounting etc. It will provide help to research scholar in summarizing their literature review in future and suggests upcoming fields which may be undertaken as future research topic. The present study is original and reference of all sources, used in the study, are given at appropriate places and duly acknowledged.
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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".