This document summarizes a study comparing field-weighted readership (FWRI) to field-weighted citations (FWCI). The key findings are:
1) There is a strong correlation between publications read and cited, though readership may be influenced by size and geographic distribution of readers.
2) FWRI and FWCI are also strongly correlated when normalized for size, removing the effect of specialization.
3) FWRI has a small advantage over FWCI in most fields, especially agriculture and social sciences. Variations between countries also tend to hold true within fields.
Field-weighting readership: how does it compare to field-weighting citations?
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Sarah Huggett, Chris James, Eleonora Palmaro
AROSIM 2018
Altmetrics for Research Outputs Measurement
and Scholarly Information Management
Field-Weighting Readership:
How Does it Compare to Field-
Weighting Citations?
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Key Findings
• There is a strong correlation between publications read (Mendeley
reads) and publications cited (Scopus citations). To some extent this
is a function of size. Mendeley readers geographic distribution may
also play a role.
• There is a strong correlation between FWRI and FWCI, thereby
removing any size effect, as well as any specialisation effect.
• For most fields except the Humanities there appears to be a small
FWRI advantage, particularly pronounced in the Agricultural and
Social Sciences. Variations per country overall tend to hold true per
field.
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Conclusions
• There is a strong correlation between number of papers cited and read
per country, which doesn’t appear to be solely a function of size.
• The correlation between FWRI and FWCI is lower but still strong.
Variations per country tend to hold true per field.
• Specific regional patterns may be influenced by the geographic
distribution of Mendeley readers.
• FWRI appears as a robust metrics that can offer a useful complement to
FWCI, in that it provides insights on a different part of the scholarly
communications cycle.
• More detailed analyses are welcome to further test the metrics at
different aggregation levels. It would also be interesting to see how it
compares to other indicator types (e.g. downloads, views, altmetrics).