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Betweenness and diversity in journal citation networks as measures of interdisciplinarity—A tribute to Eugene Garfield

Overview of attention for article published in Scientometrics, October 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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130 Mendeley
Title
Betweenness and diversity in journal citation networks as measures of interdisciplinarity—A tribute to Eugene Garfield
Published in
Scientometrics, October 2017
DOI 10.1007/s11192-017-2528-2
Pubmed ID
Authors

Loet Leydesdorff, Caroline S. Wagner, Lutz Bornmann

Abstract

Journals were central to Eugene Garfield's research interests. Among other things, journals are considered as units of analysis for bibliographic databases such as the Web of Science and Scopus. In addition to providing a basis for disciplinary classifications of journals, journal citation patterns span networks across boundaries to variable extents. Using betweenness centrality (BC) and diversity, we elaborate on the question of how to distinguish and rank journals in terms of interdisciplinarity. Interdisciplinarity, however, is difficult to operationalize in the absence of an operational definition of disciplines; the diversity of a unit of analysis is sample-dependent. BC can be considered as a measure of multi-disciplinarity. Diversity of co-citation in a citing document has been considered as an indicator of knowledge integration, but an author can also generate trans-disciplinary-that is, non-disciplined-variation by citing sources from other disciplines. Diversity in the bibliographic coupling among citing documents can analogously be considered as diffusion  or differentiation of knowledge across disciplines. Because the citation networks in the cited direction reflect both structure and variation, diversity in this direction is perhaps the best available measure of interdisciplinarity at the journal level. Furthermore, diversity is based on a summation and can therefore be decomposed; differences among (sub)sets can be tested for statistical significance. In the appendix, a general-purpose routine for measuring diversity in networks is provided.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 130 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 130 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 19%
Other 15 12%
Researcher 14 11%
Student > Master 11 8%
Student > Doctoral Student 9 7%
Other 32 25%
Unknown 24 18%
Readers by discipline Count As %
Social Sciences 35 27%
Computer Science 24 18%
Business, Management and Accounting 6 5%
Mathematics 4 3%
Engineering 4 3%
Other 23 18%
Unknown 34 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 October 2017.
All research outputs
#3,643,897
of 23,005,189 outputs
Outputs from Scientometrics
#722
of 2,690 outputs
Outputs of similar age
#66,338
of 323,110 outputs
Outputs of similar age from Scientometrics
#20
of 57 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,690 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.8. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 323,110 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.