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Subject: metrics ×

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    Eigenfactor
    (München : De Gruyter Saur, 2021) Fraumann, Grischa; D'Souza, Jennifer; Holmberg, Kim
    The Eigenfactor™ is a journal metric, which was developed by Bergstrom and his colleagues at the University of Washington. They invented the Eigenfactor as a response to the criticism against the use of simple citation counts. The Eigenfactor makes use of the network structure of citations, i.e. citations between journals, and establishes the importance, influence or impact of a journal based on its location in a network of journals. The importance is defined based on the number of citations between journals. As such, the Eigenfactor algorithm is based on Eigenvector centrality. While journal based metrics have been criticized, the Eigenfactor has also been suggested as an alternative in the widely used San Francisco Declaration on ResearchAssessment (DORA).
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    The h-index
    (München : De Gruyter Saur, 2021) Fraumann, Grischa; Mutz, Rüdiger
    The h-index is a mainstream bibliometric indicator, since it is widely used in academia, research management and research policy. While its advantages have been highlighted, such as its simple calculation, it has also received widespread criticism. The criticism is mainly based on the negative effects it may have on scholars, when the index is used to describe the quality of a scholar. The “h” means “highly-cited” and “high achievement”, and should not be confused with the last name of its inventor, Hirsch. Put simply, the h-index combines a measure of quantity and impact in a single indicator. Several initiatives try to provide alternatives to the h-index to counter some of its shortcomings.
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    What metrics best reflect the energy and carbon intensity of cities? Insights from theory and modeling of 20 US cities
    (Bristol : IOP, 2013) Ramaswami, A.; Chavez, A.
    Three broad approaches have emerged for energy and greenhouse gas (GHG) accounting for individual cities: (a) purely in-boundary source-based accounting (IB); (b) community-wide infrastructure GHG emissions footprinting (CIF) incorporating life cycle GHGs (in-boundary plus trans-boundary) of key infrastructures providing water, energy, food, shelter, mobility-connectivity, waste management/sanitation and public amenities to support community-wide activities in cities - all resident, visitor, commercial and industrial activities; and (c) consumption-based GHG emissions footprints (CBF) incorporating life cycle GHGs associated with activities of a sub-set of the community - its final consumption sector dominated by resident households. The latter two activity-based accounts are recommended in recent GHG reporting standards, to provide production-dominated and consumption perspectives of cities, respectively. Little is known, however, on how to normalize and report the different GHG numbers that arise for the same city. We propose that CIF and IB, since they incorporate production, are best reported per unit GDP, while CBF is best reported per capita. Analysis of input-output models of 20 US cities shows that GHGCIF/GDP is well suited to represent differences in urban energy intensity features across cities, while GHGCBF/capita best represents variation in expenditures across cities. These results advance our understanding of the methods and metrics used to represent the energy and GHG performance of cities.