Bibliographic record
Abstract
Open access publishing enables scholarship to be openly accessible to everyone, which has countless benefits. However, the open access movement has opened the door for "predatory publishers" to take advantage of researchers surviving in this publish or perish academic landscape. Predatory journals are becoming increasingly common. Nursing researchers, instructors, and students need to be made aware of the dangers of predatory journals, and they need to know how to identify them. While there are blacklists and whitelists that can be used to aid in decision-making, it is critical to note that these lists can never be entirely up to date. This article incorporates a literature review which provides insights into newer trends in predatory and unethical publishing, including "journal hijacking" and "bogus impact factors". Extensive criteria for assessing emerging or unknown journals is compiled to aid researchers, students, educators, and the public in evaluating open access publications.
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 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 | 0.094 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.059 | 0.133 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.053 | 0.006 |
| Open science | 0.025 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".