THE GROWING CHALLENGE OF PREDATORY PUBLISHING: A CALL FOR ACTION
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
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.
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 | 0.033 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.061 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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