Hidden Dangers: COVID-19-Based Research in Predatory Journals
Bibliographic record
Abstract
There has been a widespread increase in research on the topic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in both predatory and non-predatory journals. It is important to understand the origin, content, funding, and reach of predatory articles to gauge the detrimental effects of such articles. In this study, the authors, therefore, investigated 210 randomly selected predatory articles with COVID-19-based research published online between February 2020 and February 2021 in journals included in Cabells’ Predatory Reports. Their research shows that although India is still among the major contributors of articles published in predatory journals, predatory publishing should no longer be regarded as only a problem in the developing world. The authors also found funded research and articles reporting dangerous treatments with no clinical trials and small sample sizes with unconvincing or misleading scientific evidence.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchScholarly communicationResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchBibliometricsScholarly communicationResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.036 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.026 | 0.037 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".