Potential predatory and legitimate biomedical journals: can you tell the difference? A cross-sectional comparison
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
BACKGROUND: The Internet has transformed scholarly publishing, most notably, by the introduction of open access publishing. Recently, there has been a rise of online journals characterized as 'predatory', which actively solicit manuscripts and charge publications fees without providing robust peer review and editorial services. We carried out a cross-sectional comparison of characteristics of potential predatory, legitimate open access, and legitimate subscription-based biomedical journals. METHODS: On July 10, 2014, scholarly journals from each of the following groups were identified - potential predatory journals (source: Beall's List), presumed legitimate, fully open access journals (source: PubMed Central), and presumed legitimate subscription-based (including hybrid) journals (source: Abridged Index Medicus). MEDLINE journal inclusion criteria were used to screen and identify biomedical journals from within the potential predatory journals group. One hundred journals from each group were randomly selected. Journal characteristics (e.g., website integrity, look and feel, editors and staff, editorial/peer review process, instructions to authors, publication model, copyright and licensing, journal location, and contact) were collected by one assessor and verified by a second. Summary statistics were calculated. RESULTS: Ninety-three predatory journals, 99 open access, and 100 subscription-based journals were analyzed; exclusions were due to website unavailability. Many more predatory journals' homepages contained spelling errors (61/93, 66%) and distorted or potentially unauthorized images (59/93, 63%) compared to open access journals (6/99, 6% and 5/99, 5%, respectively) and subscription-based journals (3/100, 3% and 1/100, 1%, respectively). Thirty-one (33%) predatory journals promoted a bogus impact metric - the Index Copernicus Value - versus three (3%) open access journals and no subscription-based journals. Nearly three quarters (n = 66, 73%) of predatory journals had editors or editorial board members whose affiliation with the journal was unverified versus two (2%) open access journals and one (1%) subscription-based journal in which this was the case. Predatory journals charge a considerably smaller publication fee (median $100 USD, IQR $63-$150) than open access journals ($1865 USD, IQR $800-$2205) and subscription-based hybrid journals ($3000 USD, IQR $2500-$3000). CONCLUSIONS: We identified 13 evidence-based characteristics by which predatory journals may potentially be distinguished from presumed legitimate journals. These may be useful for authors who are assessing journals for possible submission or for others, such as universities evaluating candidates' publications as part of the hiring process.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it