The quest for an unbiased scientific impact indicator remains open

Abstract

Developing unbiased indicators of scientific impact has long been a central question in the scientometrics and science of science communities (1, 2). Ke et al. (3) recently tackled the ambitious challenge of developing a paper-level network-based indicator that can be fairly compared across time and fields even without the need for a field classification system, concluding that their proposed achieves this objective. The idea of leveraging a network-based mechanism to prevent impact indicator bias provides a compelling perspective to the long-standing debate on indicator bias, which could inspire many future works. Unfortunately, the validation performed in the paper does not properly test for bias, nor does it test properly for the indicator's ability to detect groundbreaking research.