Partition-based Field Normalization: An approach to highly specialized publication records

TL;DR

This paper introduces a partition-based field normalization method that refines reference domains for more accurate research performance assessment at the individual scientist level, especially in specialized or interdisciplinary contexts.

Contribution

It proposes a novel approach to define reference domains using partitioned subject categories, improving normalization accuracy over standard methods.

Findings

Potential to significantly alter performance metrics in specialized cases

Applicable to various existing normalization methods

Enhances accuracy for interdisciplinary research evaluation

Abstract

Field normalized citation rates are well-established indicators for research performance from the broadest aggregation levels such as countries, down to institutes and research teams. When applied to still more specialized publication sets at the level of individual scientists, also a more accurate delimitation is required of the reference domain that provides the expectations to which a performance is compared. This necessity for sharper accuracy challenges standard methodology based on predefined subject categories. This paper proposes a way to define a reference domain that is more strongly delimited than in standard methodology, by building it up out of cells of the partition created by the pre-defined subject categories and their intersections. This partition approach can be applied to different existing field normalization variants. The resulting reference domain lies between those generated by standard field normalization and journal normalization. Examples based on fictive and real publication records illustrate how the potential impact on results can exceed or be smaller than the effect of other currently debated normalization variants, depending on the case studied. The proposed Partition-based Field Normalization is expected to offer advantages in particular at the level of individual scientists and other very specific publication records, such as publication output from interdisciplinary research.