The application of bibliometrics to research evaluation in the humanities and social sciences: An exploratory study using normalized <scp>G</scp>oogle <scp>S</scp>cholar data for the publications of a research institute is a research paper published in Journal of the Association for Information Science and Technology (2016). On theSindex it has a DataRank of 4.2. It has been cited 73 times, with 68 citing works in its 1-hop citation network.
In the humanities and social sciences, bibliometric methods for the assessment of research performance are (so far) less common. This study uses a concrete example in an attempt to evaluate a research institute from the area of social sciences and humanities with the help of data from Google Scholar (GS). In order to use GS for a bibliometric study, we developed procedures for the normalization of citation impact, building on the procedures of classical bibliometrics. In order to test the convergent validity of the normalized citation impact scores, we calculated normalized scores for a subset of the publications based on data from the Web of Science (WoS) and Scopus. Even if scores calculated with the help of GS and the WoS/Scopus are not identical for the different publication types (considered here), they are so similar that they result in the same assessment of the institute investigated in this study: For example, the institute's papers whose journals are covered in the WoS are cited at about an average rate (compared with the other papers in the journals).
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.646
From this paper's citation signal
Citation Network Contribution
3.6
From 60 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 15% comes from its base citations and 85% from the citation network (60 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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