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Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics
KTH, School of Education and Communication in Engineering Science (ECE), Department for Library services, Language and ARC, Library, Publication Infrastructure.ORCID iD: 0000-0003-0229-3073
2018 (English)In: Journal of Informetrics, ISSN 1751-1577, E-ISSN 1875-5879, Vol. 12, no 1, p. 133-152Article in journal (Refereed) Published
Abstract [en]

The purpose of this study is to find a theoretically grounded, practically applicable and useful granularity level of an algorithmically constructed publication-level classification of research publications (ACPLC). The level addressed is the level of research topics. The methodology we propose uses synthesis papers and their reference articles to construct a baseline classification. A dataset of about 31 million publications, and their mutual citations relations, is used to obtain several ACPLCs of different granularity. Each ACPLC is compared to the baseline classification and the best performing ACPLC is identified. The results of two case studies show that the topics of the cases are closely associated with different classes of the identified ACPLC, and that these classes tend to treat only one topic. Further, the class size variation is moderate, and only a small proportion of the publications belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable to determine the topic granularity level of an ACPLC and that the ACPLC identified by this methodology is useful for bibliometric analyses. 

Place, publisher, year, edition, pages
Elsevier Ltd , 2018. Vol. 12, no 1, p. 133-152
Keyword [en]
Algorithmic classification, Article-level classification, Classification systems, Granularity level, Topic, Computer applications, Bibliometric analysis, Case-studies, Classification system, Different class, Different granularities, Granularity levels, Research topics, Publishing
National Category
Media and Communications
Identifiers
URN: urn:nbn:se:kth:diva-223152DOI: 10.1016/j.joi.2017.12.006ISI: 000427479800010Scopus ID: 2-s2.0-85039443998OAI: oai:DiVA.org:kth-223152DiVA, id: diva2:1190422
Note

Export Date: 13 February 2018; Article; Correspondence Address: Sjögårde, P.; University Library, Karolinska InstitutetSweden; email: peter.sjogarde@ki.se. QC QC 20180314

Available from: 2018-03-14 Created: 2018-03-14 Last updated: 2018-05-04Bibliographically approved

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Ahlgren, Per

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  • apa
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