Bibliographic record
Abstract
Predatory publishers, also known as deceptive journals or pseudo-journals, are a growing problem in scholarly communications. These counterfeit periodicals exploit the principles of open access publishing for profit and can even engage in criminal practices. Early-career professionals and scholars from developing countries are particularly vulnerable to these tactics. Consequently, pseudo-journals pose a threat to the integrity of academic research. It is imperative for scholars to be cautioned and supported by knowledgeable allies, yet there is a lack of leadership and investment in this area. While some studies have acknowledged the usefulness of librarians as advocates and guides, few have investigated the specific role of academic libraries as warriors against predatory publishers. While most libraries currently have initiatives to inform researchers about predatory publishers, the general attitude indicates that more needs to be done. Therefore, a list of feasible, sustainable recommendations was compiled. This list aims to supply library workers with possible solutions to implement in their workplaces. Academic libraries have the potential to spearhead a movement that safeguards researchers against predatory publishers, thereby upholding publication ethics and the integrity of research.
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 | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.032 | 0.029 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".