Wojciech Charemza , Michał Lewandowski , Łukasz Woźny
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引用次数: 0
Abstract
Over the last few years, ranking lists of academic journals have become one of the key indicators for evaluating individual researchers, departments and universities. How to optimally design such rankings? What can we learn from commonly used journal ranking lists? To address these questions, we propose a simple, theoretical model of optimal rewards for publication in academic journals. Based on a principal-agent model with researchers' hidden abilities, we characterize the optimal journal reward system, where all available journals are assigned to one of several categories or ranks. We provide a tractable example that has a closed-form solution and allows numerical applications. Finally, we show how to calibrate the distribution of researchers' ability levels implied by the observed journal ranking schemes.
期刊介绍：
Journal of Informetrics (JOI) publishes rigorous high-quality research on quantitative aspects of information science. The main focus of the journal is on topics in bibliometrics, scientometrics, webometrics, patentometrics, altmetrics and research evaluation. Contributions studying informetric problems using methods from other quantitative fields, such as mathematics, statistics, computer science, economics and econometrics, and network science, are especially encouraged. JOI publishes both theoretical and empirical work. In general, case studies, for instance a bibliometric analysis focusing on a specific research field or a specific country, are not considered suitable for publication in JOI, unless they contain innovative methodological elements.
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