Citation Selectivity in Retraction Literature: A Large-Scale Bibliometric Analysis

Abstract

Digital libraries enable systematic scrutiny of scholarly work, including retraction, a safeguard for flagging flawed or fraudulent publications. However, existing retraction studies primarily focus on the reliability of a paper’s own findings and offer little insight into how it engages with the literature it draws on. We investigate the referencing behavior of retracted papers and formulate citation selectivity as a novel research problem grounded in motivated reasoning, where authors may curate reference lists that echo their own claims while under-citing equally relevant but less convenient work. We introduce the reference selectivity score, which compares how closely a paper’s cited references align with its content against an independently constructed pool of expected references. Using a large-scale dataset of 42,330 matched pairs of retracted and control papers, we find that retracted papers exhibit significantly higher citation selectivity than their matched controls.