THE GROWING CHALLENGE OF PREDATORY PUBLISHING: A CALL FOR ACTION
Bibliographic record
Abstract
Predatory publishing (PP) is a growing challenge since the emergence of an online and openaccess publishing model 1 .The term PP was first coined in 2010 by Jeffrey Beall who explained that the mission of predatory publishers was "to exploit the author-pays, open-access model for their own profit" 1:15 .Publishing in predatory journals is becoming an industry that threatens the integrity of scientific discovery and scholarship 2 .PP not only wastes funding and other resources 3 , but it is also detrimental to authors' reputation and careers.It impedes meaningful knowledge dissemination due to the fact that information published in predatory journals may not be credible or reliable 4 .This is a cause for concern to nursing and the biomedical sciences when PP is cited in legitimate journals [5][6] or when they are included in evidence syntheses published in legitimate journals 7 .Such citations have the potential of altering results 7 and/or impacting patient care 8 .Of concern, the number of predatory journals continues to increase across disciplines 9 with 'no signs of slowing' -Cabells Scholarly Analytics list of suspected predatory journals includes 17,000 journal titles! 10 Although much has been written about PP, there continues to be a notable lack of empirical studies on PP across all disciplines 9 including nursing 4,9 .In this editorial, we highlight best practices for scholarly publishing, discuss current perspectives on PP, and identify strategies to halt submissions to predatory journals.
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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 | 0.054 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.032 | 0.039 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.031 | 0.037 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".