Reputation and impact in academic careers

Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here, we develop an original framework for measuring how a publication’s citation rate Δ c depends on the reputation of its central author i , in addition to its net citation count c . To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly cited scientists, using the total citations C ᵢ of each scientist as his/her reputation measure. We find a citation crossover c ×, which distinguishes the strength of the reputation effect. For publications with c < c ×, the author’s reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in C ᵢ. However, the reputation effect becomes negligible for highly cited publications meaning that, for c ≥ c ×, the citation rate measures scientific impact more transparently. In addition, we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science. Significance Over a scientist’s career, a reputation is developed, a standing within a research community, based largely upon the quantity and quality of his/her publications. Here, we develop a framework for quantifying the influence author reputation has on a publication’s future impact. We find author reputation plays a key role in driving a paper’s citation count early in its citation life cycle, before a tipping point, after which reputation has much less influence relative to the paper’s citation count. In science, perceived quality, and decisions made based on those perceptions, is increasingly linked to citation counts. Shedding light on the complex mechanisms driving these quantitative measures facilitates not only better evaluation of scientific outputs but also a more transparent evaluation of the scientists producing them.