Bibliographic record
Abstract
How many invitations did you receive last week to publish your latest research data in a journal with a name very similar to those of our classic scientific journals? The reason for this generosity must be the high chance of cheating researchers so that they pay article processing charges. These journals have been denominated ‘predatory journals’ and have interested a Canadian librarian, Charles Beall, to a very high extent. The so-called ‘Beall’s list of predatory journals’ has become widely recognized (1), and today it contains more than 1000 titles. Since the number of ‘invitations’ seems to increase constantly there must be some authors that are accepting these offers. This might also be one reason for why traditional scientific journals have been facing decreasing numbers of submissions for a while. So when such offers appear in your inbox, do consult this Beall’s list. If indeed, which most often is the case, you find the journal on that list, you should forget about submitting anything to them. There is a great risk that besides losing some money you will also lose control of your manuscript. It might just disappear in cyber space or be blocked/lost in a production process that goes on forever.
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 | 0.016 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.043 | 0.030 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.114 | 0.047 |
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".