Predatory journals and dubious publishers: how to avoid being their prey
Bibliographic record
Abstract
SUMMARY Open access publishing has a dark side, the predatory publishers and journals that exist for revenue rather than scholarly activity. This article helps researchers to: (1) identify some of the commonly used tactics and characteristics of predatory publishing; and (2) avoid falling prey to them. In summary, authors should choose the journal for submission themselves and never respond to unsolicited emails. It is also important to check blacklists such as ‘Stop Predatory Journals’ and whitelists such the Directory of Open Access Journals. LEARNING OBJECTIVES After reading this article, readers should be able to do the following: • be aware of the dangers of predatory journals and publishers • use blacklists of predatory journals and publishers’ whitelists of legitimate open access journals • be aware of warning signs that might suggest a predatory journal or publisher. DECLARATION OF INTEREST S.K. is on the editorial board of BJPsych International. He also receives five to ten spam emails a day from predatory journals and publishers.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchScholarly communication Domain: Evaluation · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Research integrityScholarly communication Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.034 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".