| 1. |
- Abramo, Giovanni, et al.
(författare)
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Retraction of Predatory publishing in Scopus : evidence on cross-country differences lacks justification
- 2023
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Ingår i: Scientometrics. - : Springer Science and Business Media LLC. - 0138-9130 .- 1588-2861. ; 128:2, s. 1459-1461
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Tidskriftsartikel (refereegranskat)abstract
- As members of the Distinguished Reviewers Board of Scientometrics and/or as recipients of the Derek de Solla Price Medal, we wish to express our disagreement with the retraction of the paper “Predatory publishing in Scopus: evidence on cross-country differences” co-authored by Vít Macháček and Martin Srholec (Macháček & Srholec, 2021a). The retraction was discussed in the blog Retraction Watch (Oransky, 2021), where important information about the context of the retraction was disclosed, including the pressure exerted by the publisher Frontiers on Scientometrics.
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| 2. |
- Ahlgren, Per, et al.
(författare)
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The correlation between citation-based and expert-based assessments of publication channels : SNIP and SJR vs. Norwegian quality assessments
- 2014
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Ingår i: Journal of Informetrics. - : Elsevier BV. - 1751-1577 .- 1875-5879. ; 8:4, s. 985-996
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Tidskriftsartikel (refereegranskat)abstract
- We study the correlation between citation-based and expert-based assessments of journals and series, which we collectively refer to as sources. The source normalized impact per paper (SNIP), the Scimago Journal Rank 2 (SJR2) and the raw impact per paper (RIP) indicators are used to assess sources based on their citations, while the Norwegian model is used to obtain expert-based source assessments. We first analyze - within different subject area categories and across such categories - the degree to which RIP, SNIP and SJR2 values correlate with the quality levels in the Norwegian model. We find that sources at higher quality levels on average have substantially higher RIP, SNIP, and SJR2 values. Regarding subject area categories, SNIP seems to perform substantially better than SJR2 from the field normalization point of view. We then compare the ability of RIP, SNIP and SJR2 to predict whether a source is classified at the highest quality level in the Norwegian model or not. SNIP and SJR2 turn out to give more accurate predictions than RIP, which provides evidence that normalizing for differences in citation practices between scientific fields indeed improves the accuracy of citation indicators.
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| 3. |
- Sjogarde, Peter, et al.
(författare)
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Algorithmic labeling in hierarchical classifications of publications : Evaluation of bibliographic fields and term weighting approaches
- 2021
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Ingår i: Journal of the Association for Information Science and Technology. - : John Wiley & Sons. - 2330-1635 .- 2330-1643. ; 72:7, s. 853-869
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Tidskriftsartikel (refereegranskat)abstract
- Algorithmic classifications of research publications can be used to study many different aspects of the science system, such as the organization of science into fields, the growth of fields, interdisciplinarity, and emerging topics. How to label the classes in these classifications is a problem that has not been thoroughly addressed in the literature. In this study, we evaluate different approaches to label the classes in algorithmically constructed classifications of research publications. We focus on two important choices: the choice of (a) different bibliographic fields and (b) different approaches to weight the relevance of terms. To evaluate the different choices, we created two baselines: one based on the Medical Subject Headings in MEDLINE and another based on the Science-Metrix journal classification. We tested to what extent different approaches yield the desired labels for the classes in the two baselines. Based on our results, we recommend extracting terms from titles and keywords to label classes at high levels of granularity (e.g., topics). At low levels of granularity (e.g., disciplines) we recommend extracting terms from journal names and author addresses. We recommend the use of a new approach, term frequency to specificity ratio, to calculate the relevance of terms.
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