PIXABAY, OPENCLIPARTVECTORSDuring the course of their careers, many scientists criticize the work of others—pointing out flaws, inconsistencies, or contradictions—in the literature. This is part of scientific progress. A proof-of-concept study now describes a research tool for recognizing these so-called negative citations, making it possible to contextualize and study them on a larger scale than possible before.
According to the results, published today (October 26) in PNAS, papers pay only a slight long-term penalty in the total number of citations they receive after a negative one. That criticized papers continue to garner citations over time suggests that it’s better to receive negative attention than none at all.
Until now, the best way of studying negative citations was by reading and coding each individual article—something Jeffrey Furman, an associate professor of strategy and innovation at Boston University, said he tried before, while analyzing the impact of retracted studies. In contrast, the new method, which employs natural language processing and machine learning, “could have saved me dozens of hours, if only I had waited,” said Furman, who was not involved with the study. “Coming ...
