Knowledge and motivations of researchers publishing in presumed predatory journals: a survey
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
OBJECTIVES: To develop effective interventions to prevent publishing in presumed predatory journals (ie, journals that display deceptive characteristics, markers or data that cannot be verified), it is helpful to understand the motivations and experiences of those who have published in these journals. DESIGN: An online survey delivered to two sets of corresponding authors containing demographic information, and questions about researchers' perceptions of publishing in the presumed predatory journal, type of article processing fees paid and the quality of peer review received. The survey also asked six open-ended items about researchers' motivations and experiences. PARTICIPANTS: Using Beall's lists, we identified two groups of individuals who had published empirical articles in biomedical journals that were presumed to be predatory. RESULTS: Eighty-two authors partially responded (~14% response rate (11.4%[44/386] from the initial sample, 19.3%[38/197] from second sample) to our survey. The top three countries represented were India (n=21, 25.9%), USA (n=17, 21.0%) and Ethiopia (n=5, 6.2%). Three participants (3.9%) thought the journal they published in was predatory at the time of article submission. The majority of participants first encountered the journal via an email invitation to submit an article (n=32, 41.0%), or through an online search to find a journal with relevant scope (n=22, 28.2%). Most participants indicated their study received peer review (n=65, 83.3%) and that this was helpful and substantive (n=51, 79.7%). More than a third (n=32, 45.1%) indicated they did not pay fees to publish. CONCLUSIONS: This work provides some evidence to inform policy to prevent future research from being published in predatory journals. Our research suggests that common views about predatory journals (eg, no peer review) may not always be true, and that a grey zone between legitimate and presumed predatory journals exists. These results are based on self-reports and may be biased thus limiting their interpretation.
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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 integrity Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Scholarly communication Domain: not available · 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 | 0.174 | 0.243 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.034 | 0.116 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.004 |
| 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 it