The escalating crisis in peer review was exacerbated by a notable surge—almost tantamount to a deluge— in the number of articles requiring assessment. This paper deluge has not been matched by an equivalent rise in the availability of experienced reviewers, who are essential for this crucial process. As underscored in the recent study by Horta and Jung (2024) titled 'The Crisis of Peer Review: A Component of Scientific Evolution,' this predicament often forces editors to turn to early-career researchers, who may lack extensive publishing experience, leaving them with few alternatives.
In this context, it's worth highlighting a recent research paper published in the Elsevier journal 'Expert Systems with Applications,' authored by scholars from academic institutions in Switzerland and China. Their reviewer-reputation ranking (RRR) algorithm crafted to identify high-quality papers during the review process, assigns greater weight to reviewers with higher reputation scores. Extensive testing on both artificial and real-world datasets, totaling over 300,000 papers, has demonstrated the algorithm's superior performance in identifying top-quality manuscripts:
