A decade in hijacked journals: what will be the future trend?
Bibliographic record
Abstract
Purpose: Hijacked journals are fraudulent websites that mimic legitimate journals and, by charging authors, publish manuscripts. The current editorial endeavors to provide a close view of current literature. This editorial piece analyzes 10 years of research on hijacked journals and endeavors to shed light on future trends. Methods: Current research uses a bibliometric approach to analyze data and discuss results. The OpenAlex has been used for data collection. Some of the data analysis was conducted using OpenAlex. The other study was done using Bibliometrix, and the date is limited to publication between 2014 and 2024. Results: The findings provide a close view of the published literature in terms of access type, growth, topics, most frequent words, country contribution, top publishers, and alignment of literature with sustainable development goals. Conclusion: The gap in current literature is the limitation in easily usable methods to be accessible by all researchers for hijacked journal detection and data analysis. The use of artificial intelligence can be promising.
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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".