Global Landscape of the Attack of Predatory Journals in Oncology
Bibliographic record
Abstract
PURPOSE Open-access publishing expanded opportunities to give visibility to research results but was accompanied by the proliferation of predatory journals (PJos) that offer expedited publishing but potentially compromise the integrity of research and peer review. To our knowledge, to date, there is no comprehensive global study on the impact of PJos in the field of oncology. MATERIALS AND METHODS A 29 question-based cross-sectional survey was developed to explore knowledge and practices of predatory publishing and analyzed using descriptive statistics and binary logistic regression. RESULTS Four hundred and twenty-six complete responses to the survey were reported. Almost half of the responders reported feeling pressure to publish from supervisors, institutions, and funding and regulatory agencies. The majority of authors were contacted by PJos through email solicitations (67.8%), with fewer using social networks (31%). In total, 13.4% of the responders confirmed past publications on PJo, convinced by fast editorial decision time, low article-processing charges, limited peer review, and for the promise of academic boost in short time. Over half of the participants were not aware of PJo detection tools. We developed a multivariable model to understand the determinants to publish in PJos, showing a significant correlation of practicing oncology in low- and middle-income countries (LMICs) and predatory publishing (odds ratio [OR], 2.02 [95% CI, 1.01 to 4.03]; P = .04). Having previous experience in academic publishing was not protective (OR, 3.81 [95% CI, 1.06 to 13.62]; P = .03). Suggestions for interventions included educational workshops, increasing awareness through social networks, enhanced research funding in LMICs, surveillance by supervisors, and implementation of institutional actions against responsible parties. CONCLUSION The prevalence of predatory publishing poses an alarming problem in the field of oncology, globally. Our survey identified actionable risk factors that may contribute to vulnerability to PJos and inform guidance to enhance research capacity broadly.
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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 | MetaresearchResearch integrityBibliometrics Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchBibliometricsResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.133 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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.
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".