Disaggregated research evaluation through median-based characteristic scores and scales: a comparison with the mean-based approach

Characteristic scores and scales (CSS) were proposed in the late 1980s as a powerful tool in evaluative scientometrics but have only recently begun to be used for systematic, multi-level appraisal. By relying on successive sample means found in citation distributions the CSS method yields performance classes that can be used to benchmark individual units of assessment. This article investigates the theoretical and empirical consequences of a median-based approach to the construction of CSS. Mean and median-based CSS algorithms developed in the R language and environment for statistical computing are applied to citation data of papers from journals indexed in four Web of Science categories: Information Science and Library Science, Social work, Microscopy and Thermodynamics. Subject category-level and journal-level comparisons highlight the specificities of the median-based approach relative to the mean-based CSS. When moving from the latter to the former substantially fewer papers are ascribed to the poorly cited CSS class and more papers become fairly, remarkably or outstandingly cited. This transition is also marked by the well-known “Matthew effect” in science. Both CSS versions promote a disaggregated perspective on research evaluation but differ with regard to emphasis: mean-based CSS promote a more exclusive view of excellence; the median-based approach promotes a more inclusive outlook.