FAIR Metrics for Motivating Ethics in Peer Review

Every year, peer reviewers perform countless hours of uncompensated, anonymous labor in order to maintain the integrity of the scholarly literature. However, the high volume of research output in need of review and the scarcity of experts' time make it difficult to maintain the quality of peer review. We previously introduced Fair Attribution to Indexed Reports (FAIR) Metrics that quantify how well a scholarly work cites and discusses prior literature, how many novel concepts it introduces, and how free it is of plagiarism and misattributions. Unlike lexical plagiarism detection, FAIR Metrics analysis relies on identifying statements with equivalent meanings. Using the FAIR Metrics module of the PDP-DREAM Ontology, we recorded the analyses in searchable, machine-readable FAIR Metrics semantic records. This approach has the potential to strengthen the integrity of scholarly publishing by providing a more transparent and systematic way to trace the origins of ideas. Furthermore, FAIR Metrics analysis can provide a basis for integrated multimedia idea plagiarism detection. Figures and tables often serve as visual abstracts that convey the most important points of a work, making their inclusion necessary for a complete analysis of a paper. Instead of having separate metrics of similarity for comparing prose text, tables, figures, and other forms of media, FAIR Metrics analysis involves extracting the claims that each part of the work is communicating. In the present work, we define new FAIR Metrics for assessing the quality of peer review, extend the FAIR Metrics module of the PDP-DREAM Ontology with the additional classes and properties needed to record FAIR Metrics analysis of a review, and demonstrate usage with three example reviews.