Universality of scholarly impact metrics
arXiv:1305.6339 · doi:10.1016/j.joi.2013.09.002
Abstract
Given the growing use of impact metrics in the evaluation of scholars, journals, academic institutions, and even countries, there is a critical need for means to compare scientific impact across disciplinary boundaries. Unfortunately, citation-based metrics are strongly biased by diverse field sizes and publication and citation practices. As a result, we have witnessed an explosion in the number of newly proposed metrics that claim to be "universal." However, there is currently no way to objectively assess whether a normalized metric can actually compensate for disciplinary bias. We introduce a new method to assess the universality of any scholarly impact metric, and apply it to evaluate a number of established metrics. We also define a very simple new metric hs, which proves to be universal, thus allowing to compare the impact of scholars across scientific disciplines. These results move us closer to a formal methodology in the measure of scholarly impact.
Accepted in Journal of Informetrics
References in corpus (5)
- Universality of citation distributions: towards an objective measure of scientific impact
- A principal component analysis of 39 scientific impact measures
- A reverse engineering approach to the suppression of citation biases reveals universal properties of citation distributions
- National Scientific Facilities and Their Science Impact on Non-Biomedical Research
- On the meaning of the h-index
Cited by in corpus (11)
- Collective credit allocation in science
- Identification of milestone papers through time-balanced network centrality
- Universality of citation distributions for academic institutions and journals
- Quality versus quantity in scientific impact
- Citation score normalized by cited references (CSNCR): The introduction of a new citation impact indicator
- Evaluating the impact of interdisciplinary research: a multilayer network approach
- Grand Challenges in Measuring and Characterizing Scholarly Impact
- Hierarchical networks of scientific journals
- Model-based evaluation of scientific impact indicators
- Prediction Methods and Applications in the Science of Science: A Survey
- Investigating the contribution of author- and publication-specific features to scholars' h-index prediction