An AI instrument can display screen hundreds of journals, and determine ones that violate high quality requirements.Credit score: PaulPaladin/Alamy
Researchers have recognized greater than 1,000 probably problematic open-access journals utilizing a synthetic intelligence (AI) instrument that screened round 15,000 titles for indicators of doubtful publishing practices.
The method, described in Science Advances on 27 August1, may very well be used to assist sort out the rise in what the research authors name “questionable open-access journals” — those who cost charges to publish papers with out doing rigorous peer overview or high quality checks.
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Not one of the journals flagged by the instrument has beforehand been on any type of watchlist, and a few titles are owned by giant, respected publishers. Collectively, the journals have revealed a whole bunch of hundreds of analysis papers which have obtained tens of millions of citations.
The research means that “there’s a complete group of problematic journals in plain sight which are functioning as supposedly revered journals that basically don’t deserve that qualification”, says Jennifer Byrne, a research-integrity sleuth and most cancers researcher on the College of Sydney, Australia.
The instrument is out there on-line in a closed beta model, and organizations that index journals, or publishers, can use it to overview their portfolios, says research co-author Daniel Acuña, a pc scientist on the College of Colorado Boulder. However, he provides, the AI generally makes errors, and isn’t designed to exchange detailed evaluations of journals and particular person publications that may end in a title being faraway from an index. “A human knowledgeable ought to be a part of the vetting course of” earlier than any motion is taken, he says.
Screening journals
The AI instrument can analyse an unlimited quantity of knowledge from journals’ web sites and the papers they publish, and seek for crimson flags — corresponding to quick turnaround instances for publishing articles and excessive charges of self-citation. It additionally assesses whether or not members of a journal’s editorial board are affiliated with well-known, respected analysis establishments, and checks how clear publications are about licensing and charges. A number of of the standards used to coach the instrument come from best-practice steering developed by the Listing of Open Entry Journals (DOAJ), an index of open-access journals run by the non-profit DOAJ Basis in Roskilde, Denmark.
Cenyu Shen, the DOAJ’s deputy head of editorial high quality, who relies in Helsinki, says that the variety of problematic journals is rising, and that their “ways have gotten extra refined”. “We’re observing extra situations the place questionable publishers purchase respectable journals, or the place paper mills buy journals to publish low-quality work,” she provides. (Paper mills are companies that promote faux papers and authorships.)
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The DOAJ’s personal high quality checks on journals are accomplished largely manually and are initiated solely after receiving complaints. In 2024, the listing investigated 473 journals, an increase of 40% in contrast with 2021. “The time our staff spent on these investigations additionally grew considerably by almost 30%, to 837 hours,” says Shen.
AI instruments may assist to hurry up a few of these assessments, Acuña says. He and his colleagues educated their mannequin on 12,869 journals which are at the moment listed within the DOAJ as respectable, in addition to 2,536 that the listing had flagged as violating its high quality requirements.
When the researchers requested the AI to judge 15,191 open-access journals listed within the public database Unpaywall, it recognized 1,437 journals as questionable. The staff estimated that some 345 of those had been mistakenly flagged: they included discontinued titles, ebook collection and journals from small, learned-society publishers. The researchers additionally discovered that the instrument had did not flag an extra 1,782 questionable journals, based mostly on estimates of error charges.