Algorithmically generated subject categories based on citation relations: An empirical micro study using papers on overall water splitting
Robin Haunschild, Hermann Schier, Werner Marx, and Lutz Bornmann

TL;DR
This study evaluates an algorithmic classification system based on citation relations for water splitting research, comparing it with traditional classifications and analyzing its effectiveness in identifying relevant papers and citation impact.
Contribution
The paper provides an empirical assessment of the ACCS, a citation-based classification system, and compares its performance with traditional intellectual classifications.
Findings
ACCS shows limited discriminatory power for water splitting papers.
Traditional classifications outperform ACCS in identifying relevant papers.
Citation impact varies across ACCS clusters and traditional categories.
Abstract
One important reason for the use of field categorization in bibliometrics is the necessity to make citation impact of papers published in different scientific fields comparable with each other. Raw citations are normalized by using field-normalization schemes to achieve comparable citation scores. There are different approaches to field categorization available. They can be broadly classified as intellectual and algorithmic approaches. A paper-based algorithmically constructed classification system (ACCS) was proposed which is based on citation relations. Using a few ACCS field-specific clusters, we investigate the discriminatory power of the ACCS. The micro study focusses on the topic "overall water splitting" and related topics. The first part of the study investigates intellectually whether the ACCS is able to identify papers on overall water splitting reliably and validly. Next, we…
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