Predatory Journals on Trial: Allegations, Responses, and Lessons for Scholarly Publishing from <i>FTC v. OMICS</i>
Bibliographic record
Abstract
On 25 August 2016, the US Federal Trade Commission (FTC) sued OMICS Group Inc., iMedPub LLC, Conference Series LLC, and Srinubabu Gedela, all affiliated with open access mega-publisher OMICS International, for deception in their solicitation of journal articles and advertising of conferences. The ongoing lawsuit seeks to stop OMICS’s deceptive practices and disgorge US $50.5 million in ill-gotten gains. OMICS has in turn claimed over $2.1 billion for harm caused by the lawsuit to its business and employees. This article describes the main arguments, counter-arguments, and court decisions in the 5920 pages of pleadings, exhibits, and orders that have been filed through 14 October 2018. The article then evaluates the case to formulate key take-aways for publishers, editors, academics, and universities. Depending on its ultimate outcome, the case against OMICS may be a turning point in the practices of questionable open access online publishers, making this interim case assessment pertinent to all concerned about the future of academic publishing.
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 | 0.055 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.037 | 0.025 |
| Scholarly communication | 0.043 | 0.021 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.064 | 0.051 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".