Predatory and Legitimate Open Access Journals in Language and Linguistics: Where do they Part Ways?
Bibliographic record
Abstract
This study aimed to identify editorial features that can distinguish predatory and legitimate open access journals in the discipline of language and linguistics. Fifty-six journals from the Directory of Open Access Journals (DOAJ) and an equal number of journals from Beall’s updated list of potential predatory journals (PPJs) were selected for a close examination. Analyses showed that these two groups of journals differed markedly in a large number of editorial features: certain publication frequencies, contact address and contact information, mean number of articles published per year, specialized focus, mean peer review time, claimed adoption of peer review, submission mode, listing of editor(s)-in-chief, relevance of their expertise, mean number of editorial board members, availability of the guide for authors and aims/scope sections, an APC for open access, mean APC, claimed indexation by DOAJ, provision of ethical guidelines and publishing policies, and presence of DOIs. Nevertheless, they did not differ significantly with regard to mean years of editorial activity, mention of average peer review time, mention of acceptance rate, mean number of editorial board members, mean number of editors, listing of editorial boards, claimed indexation by Google Scholar/ERIC/Scopus/Web of Science, COPE membership, and availability of ISSNs. These findings point to distinguishing editorial features that language and linguistics scholars need to consider when they look for legitimate open access journals to disseminate their research.
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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 | 0.026 | 0.184 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".