Disseminating Biomedical Research: Predatory Journals and Practices
Bibliographic record
Abstract
Predatory journals are journals that do not adhere to best editorial and publication practices.They often provide false or misleading information.Similarly, predatory journals have a long history of sending often aggressive and indiscriminate invitations to submit articles to them.Finally, these journals lack transparency regarding their operations.There are a large number of predatory journals that include hundreds of thousands of articles, including millions of participants who have participated in clinical research and thousands of animals included in preclinical research.The quality of reporting of these articles is disturbingly low.Unfortunately, these articles have been included in systematic reviews, meta-analyses and health policy documents.The extent to which the inclusion of these articles influence clinical practice guidelines and health policy is unknown.It is unlikely to be a zero influence.Similarly, these articles have managed to leak into what is considered trusted resources, such as PubMed.To combat the proliferation of predatory publishers and journals requires collaborative efforts on the part of many groups.Researchers need more education and resources about predatory journals.They need to be cautioned about responding to the aggressive and unsolicited E-mails they receive from these journals.Funders need to be more explicit about not allowing the use of article processing fees for publishing in predatory journals.Universities, other research organizations, and their respective libraries need to enhance their outreach concerning the problems of predatory journals and publishers.Similarly, there needs to be stronger guards against using publications from predatory journals in hiring, promotion and tenure portfolios.Finally, the research ecosystem should move away from conceptualizing whether journals are predatory or not, to a more nuanced view whereby journals and publishers are judged on their practices-high risk to lower risk.
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.
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 | Not applicable | low |
| gpt | Scholarly communicationResearch integrity Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.125 | 0.200 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.068 | 0.062 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".