Review of Research on Predatory Scientific Publications from Scopus Database between 2012 and 2022
Bibliographic record
Abstract
Since the emergence of the internet and open science in the 1990s, “predatory journals,” or “predatory publishing,” have attracted the increasing attention of scholars. Research on the topic has grown at a rapid rate, particularly in the last five years. This article serves as the first bibliometric review on the topic of “predators in the scientific publication” and draws on 869 published articles from the Scopus database between 2012 and 30 March 2022. These papers were produced by a total of 1586 authors, coming from 101 countries, representing 1538 organizations, and published in 501 journals. Research disciplines mostly covered the fields of medicine, social sciences, and nursing. This study also reveals the complexity of issues and research trends around the topic of predatory scientific publications, including the review process for scientific journals, publication fees and article processing charges, open science and open-access publications, and the like and related topics such as the impact on scholars in developing countries and academic ethics. Finally, this article provides several recommendations, namely, the need for more efficient criteria to evaluate the quality of scientific journals, more public communication on the importance of ethics in research and publication, and a greater awareness among scholars and organizations of the implications of the “predator” issue in scientific publishing.
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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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.088 | 0.107 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".