Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge?
Not on the map. Why?
Abstract (arXiv)
In 1965, Derek de Solla Price foresaw the day when a citation-based taxonomy of science and technology would be delineated and correspondingly used for science policy. A taxonomy needs to be comprehensive and accurate if it is to be useful for policy making, especially now that policy makers are utilizing citation-based indicators to evaluate people, institutions and laboratories. Determining the accuracy of a taxonomy, however, remains a challenge. Previous work on the accuracy of partition solutions is sparse, and the results of those studies, while useful, have not been definitive. In this study we compare the accuracies of topic-level taxonomies based on the clustering of documents using direct citation, bibliographic coupling, and co-citation. Using a set of new gold standards - articles with at least 100 references - we find that direct citation is better at concentrating references than either bibliographic coupling or co-citation. Using the assumption that higher concentrations of references denote more accurate clusters, direct citation thus provides a more accurate representation of the taxonomy of scientific and technical knowledge than either bibliographic coupling or co-citation. We also find that discipline-level taxonomies based on journal schema are highly inaccurate compared to topic-level taxonomies, and recommend against their use.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Nov 2015.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
4 papers.
- 2025AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Researchcited by 3
- 2024DeepDiveAI: Identifying AI Related Documents in Large Scale Literature Datacited by 2
- 2021Generating Local Maps of Science using Deep Bibliographic Couplingcited by 1
- 2022Artificial intelligence technologies to support research assessment: A reviewcited by 1
- 2025AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research17 May 2025
- 2024DeepDiveAI: Identifying AI Related Documents in Large Scale Literature Data23 Aug 2024
- 2022Artificial intelligence technologies to support research assessment: A review11 Dec 2022
- 2021Generating Local Maps of Science using Deep Bibliographic Coupling21 Sep 2021