Comparison of publication-level approaches to ex-post citation normalization

In this paper, we compare two sophisticated publication-level approaches to ex-post citation normalization: an item-oriented approach and an approach falling under the general algorithmically constructed classification system approach. Using articles published in core journals in Web of Science (SCIE, SSCI & A&HCI) during 2009 (n = 955,639), we first examine, using the measure Proportion explained variation (PEV), to what extent the publication-level approaches can explain and correct for variation in the citation distribution that stems from subject matter heterogeneity. We then, for the subset of articles from life science and biomedicine (n = 456,045), gauge the fairness of the normalization approaches with respect to their ability to identify highly cited articles when subject area is factored out. This is done by utilizing information from publication-level MeSH classifications to create high quality subject matter baselines and by using the measure Deviations from expectations (DE). The results show that the item-oriented approach had the best performance regarding PEV. For DE, only the most fine-grained clustering solution could compete with the item-oriented approach. However, the item-oriented approach performed better when cited references were heavily weighted in the similarity calculations.