Bibliographic record
Abstract
Abstract Publication in scientific journals remains the primary method to disseminate research findings; however, the landscape of scientific publication is rapidly changing. For instance, although open access publication has led to unprecedented opportunities to share information with the global scientific community, it has also contributed to the rise of “predatory journals.” These journals accept fees to publish articles without promised quality checks (e.g. peer review). In order to better understand current publication practices and the threat predatory journals pose, this session will: 1) Briefly summarize the history of scientific publication and how the current model of peer-reviewed publication developed. 2) Define predatory journals and review components of the international consensus definition (false or misleading information, deviation from best editorial and publication practices, lack of transparency, aggressive/indiscriminate solicitation; Nature doi.org/10.1038/d41586-019-03759-y). 3) Summarize empirical studies that have assessed the current burden of predatory journals. A broad group of stakeholders are affected by these journals, including researchers and the public. 4) Provide a practical approach for audience members to distinguish between predatory and legitimate journals. 5) Highlight some key developments that will lead to new publication models in the future.
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
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 | 0.044 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".