Low-quality papers are surging by exploiting public data sets and AI
Paper mills are also likely contributing to “false discoveries”
Last year, Matt Spick began to notice oddly similar papers flooding in for peer review at Scientific Reports, where he is an associate editor. He smelled a rat. The papers all drew on a publicly available U.S. data set: the National Health and Nutrition Examination Survey (NHANES), which through health exams, blood tests, and interviews has collected dietary information and other health-related measurements from more than 130,000 people. “I was getting so many nearly identical papers—one a day, sometimes even two a day,” says Spick, a statistician at the University of Surrey.
What he was seeing at his one journal is part of a larger problem, Spick has discovered. In recent years, there has been a drastic surge in poor-quality papers using NHANES, possibly spearheaded by illicit moneymaking enterprises known as paper mills and facilitated by the use of artificial intelligence (AI)-generated text, he and colleagues reported in PLOS Biology last week. The finding suggests large public health data sets are ripe for exploitation, they say.
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P.S. From me: It is necessary to develop methods for analyzing scientific texts based on AI in order to automatically filter out low-quality AI-synthesized texts before peer review. The Russian Dissernet has extensive experience in analyzing low-quality and borrowed scientific texts of dissertations from Russia and the former Soviet Union. It is necessary to use their developments to combat this new phenomenon.
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