Scholarly communication in <i>Journal of Financial Crime</i>, 2006‐2010: a bibliometric study
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the scholarly communications in Journal of Financial Crime (JFC) during the last five years and to study the key dimensions of its publication trends. Design/methodology/approach For the analysis of the study, five volumes containing 20 issues of Journal of Financial Crime during the years 2006 to 2010 have been taken up for evaluation. The authors employ necessary bibliometric measures to analyze different publication parameters. Findings It is found that the contribution of articles to each volume of JFC is very consistent and the journal has published around 30 articles per year. Single authored papers are found to be the highest, followed by two‐authored and then three‐authored papers. The degree of collaboration in JFC is found to be 0.246. In regards to ranking of country productivity, the UK topped the list followed by the USA, Canada and Australia. Journal of Financial Crime, which is the source journal, leads the table followed by Journal of Business Ethics, Crime Law and Social Change and Journal of Money Laundering Control. Research limitations/implications This paper focuses on the publication traits of Journal of Financial Crime over a five‐year period. Patterns of research output in 155 publications are analyzed. Further studies can include other journals in the field of economics. Practical implications Scholars can benefit from insights into the scholarly contributions of Journal of Financial Crime that has accommodated 220 authors from 41 different countries of the world. Originality/value The paper provides valuable insights into the nature of academic publishing of Journal of Financial Crime. It can help JFC readers to understand the most striking contributions, highly cited journals, the most prolific authors, country productivity, and assorted parameters.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.063 | 0.094 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".