Bibliographic record
Abstract
A recent article suggested that authors should avoid publishing with OMICS, as they have been producing fake articles. Moreover, other publishers often conceal their association with OMICS as the scholarly community is wary of OMICS after a US court fined them US$50.1 million for deceptive practices. In addition to the 15 publishers previously mentioned (which included OMICS), another five have been identified. Evidence is presented for these ninteen publishers to show their relationship with OMICS. This is the first time that such an evidence base has been presented in a peer-reviewed article. Several small case studies are also presented to demonstrate that some of the publishers are not operating in ways that we would expect of high-quality publishers, noting that further investigation would be useful. Supplementary files are provided, providing details of the web pages that have been archived, as well as other data collected as part of this research.
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 | 0.217 | 0.544 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.031 | 0.024 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 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; the direct Gemma label and the distilled Codex classifier 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".