Ai Flags 1,000+ ‘fake’ Journals – Can We Still Trust “peer-reviewed” Science?

Researchers person utilized AI to surface astir 15,000 open-access journals and flagged much than 1,000 arsenic perchance problematic, according to a study from nan University of Colorado Boulder, published August 27 successful Science Advances.

The instrumentality spots reddish flags for illustration ultra-fast publication times, precocious self-citation, and opaque fees – and moreover flagged titles owned by big, reputable publishers.

How nan AI useful erstwhile screening subject journals

The strategy scans diary websites and published papers for patterns tied to dubious practice. It was trained successful portion connected best-practice criteria from nan Directory of Open Access Journals (DOAJ). This is nan main watchdog and directory for trustworthy open-access journals.

Co-author of nan University of Colorado Boulder study, Daniel Acuña, stresses nan instrumentality is simply a prescreener, not judge and jury, “A quality master should beryllium portion of nan vetting process” earlier immoderate action is taken. (Cited by Nature.)

However, crab interrogator astatine nan University of Sydney, Australia Jennifer Byrne, said, “there’s a full group of problematic journals successful plain show that are functioning arsenic supposedly respected journals that really don’t merit that qualification.”

The University of Colorado Boulder squad adds that they “tried to make [the AI] arsenic interpretable arsenic possible,” and framework it arsenic a “firewall for science.”

What precisely is simply a “peer-reviewed” study?

The building “peer-reviewed study” sewage thrown astir a batch during nan Covid pandemic. A elemental meaning is that a adjacent reappraisal refers to nan information of a manuscript by an author’s peers – independent experts who measure whether a study is sound earlier it’s published. Major journals usage it to protect reliability and reputation.

But adjacent reappraisal isn’t flawless. Different models (single-blind, double-blind, open) person pros and cons, and history shows it tin miss errors aliases beryllium gamed, which is precisely why detecting questionable journals matters.

Follow nan money – who costs research?

A broad scoping reappraisal successful nan American Journal of Public Health found, “Industry-sponsored studies thin to beryllium biased successful favour of nan sponsor’s products.” It besides concluded, “Corporate interests tin thrust investigation agendas distant from questions that are nan astir applicable for nationalist health.”

The aforesaid reappraisal documented communal strategies crossed industries (tobacco, food, pharma): steer backing toward commercially useful topics, prioritise lines of enquiry that support legal/policy positions, and build credibility done publications and conferences.

How should you measure “scientific” claims?

• Check nan diary – is it indexed by DOAJ?
• Does nan tract intelligibly picture peer-review policies, fees, and licences? (The AI flagged journals for precisely these gaps.)
• Look for nan peer-review trail: Do editors sanction reviewers aliases people reports (transparent/open review), aliases is nan process opaque?
• Follow backing disclosures: Who paid? Are conflicts declared? Funding tin displacement investigation agendas and outcomes.
• Beware velocity and spam: Ultra-fast acceptances and wide inducement emails are reddish flags.

AI tin spotlight anomalies astatine scale, specified arsenic journals pinch overseas citation patterns, suspicious turnaround times, and murky governance. If utilized well, it whitethorn go a powerful early-warning system. But moreover its creators insist connected last quality judgement. Tools don’t show america what’s true; they thief america determine what deserves our attention.

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