Predatory Journals on Trial: Allegations, Responses, and Lessons for Scholarly Publishing from <i>FTC v. OMICS</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.100 | 0.388 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.554 | 0.604 |
| Open science | 0.010 | 0.001 |
| Research integrity | 0.001 | 0.010 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it