Estimating the predictability of questionable open-access journals

Estimating the predictability of questionable open-access journals
Science Advances; by Han Zhuang, Lizhen Liang, Daniel E. Acuna; 8/25
Questionable journals threaten global research integrity, yet manual vetting can be slow and inflexible. Here, we explore the potential of artificial intelligence (AI) to systematically identify such venues by analyzing website design, content, and publication metadata. Evaluated against extensive human-annotated datasets, our method achieves practical accuracy and uncovers previously overlooked indicators of journal legitimacy... Our study defines “questionable open-access journals” as journals violating the best practices outlined by the Directory of Open Access Journals (DOAJ) and showing indicators of low editorial standards.
Publisher's note: The authors use AI to evaluate open-access journals adherence to best publishing practices - an interesting use of AI that could be applied to many other settings. The list of open-access journals can be found here.