A decade in hijacked journals: what will be the future trend?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it