Bibliographic record
Abstract
Academic researchers who seek to publish their work are confronted daily with a barrage of e-mails from aggressive marketing campaigns that solicit them to publish their research with a specialized, often newly launched, journal. Known as predatory journals, they often promise high editorial and publishing standards, yet their exploitive business models, poor quality control, and minimal overall transparency victimize those researchers with limited academic experience and pave the way for low-quality articles that threaten the foundation of evidence-based research. Understanding how to identify these predatory journals requires thorough due diligence on the part of the submitting authors, and a commitment by reputable publishers, institutions, and researchers to publicly identify these predators and eliminate them as a threat to the careers of young scientists seeking to disseminate their work in scholarly journals. Anat Sci Educ 10: 392-394. © 2016 American Association of Anatomists.
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 | 0.037 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".