Social Network Analytics for Advanced Bibliometrics: Referring to Actor Roles of Management Journals instead of Journal Rankings

Impact factors are commonly used to assess journals relevance. This implies a simplified view on science as a single-stage linear process. Therefore, few top-tier journals are one-sidedly favored as outlets, such that submissions to top-tier journals explode whereas others are short of submissions. Consequently, the often claimed gap between research and practical application in application-oriented disciplines as business administration is not narrowing but becoming entrenched. A more complete view of the scientific system is needed to fully capture journals ´ contributions in the development of a discipline. Simple citation measures, as e.g. citation counts, are commonly used to evaluate scientific work. There are many known dangers of miss- or over-interpretation of such simple data and this paper adds to this discussion by developing an alternative way of interpreting a discipline based on the positions and roles of journals in their wider network. Specifically, we employ ideas from the network analytic approach. Relative positions allow the direct comparison between different fields. Similarly, the approach provides a better understanding of the diffusion process of knowledge as it differentiates positions in the knowledge creation process. We demonstrate how different modes of social capital create different patterns of action that require a multidimensional evaluation of scientific research. We explore different types of social capital and intertwined relational structures of actors to compare journals with different bibliometric profiles. Ultimately, we develop a multi-dimensional evaluation of actor roles based upon multiple indicators and we test this approach by classifying management journals based on their bibliometric environment

Tree diversity and species identity effects on soil fungi, protists and animals are context dependent

Plant species richness and the presence of certain influential species (sampling effect) drive the stability and functionality of ecosystems as well as primary production and biomass of consumers. However, little is known about these floristic effects on richness and community composition of soil biota in forest habitats owing to methodological constraints. We developed a DNA metabarcoding approach to identify the major eukaryote groups directly from soil with roughly species-level resolution. Using this method, we examined the effects of tree diversity and individual tree species on soil microbial biomass and taxonomic richness of soil biota in two experimental study systems in Finland and Estonia and accounted for edaphic variables and spatial autocorrelation. Our analyses revealed that the effects of tree diversity and individual species on soil biota are largely context dependent. Multiple regression and structural equation modelling suggested that biomass, soil pH, nutrients and tree species directly affect richness of different taxonomic groups. The community composition of most soil organisms was strongly correlated due to similar response to environmental predictors rather than causal relationships. On a local scale, soil resources and tree species have stronger effect on diversity of soil biota than tree species richness per se

Do Scientific Advancements Lean on the Shoulders of Giants? A Bibliometric Investigation of the Ortega Hypothesis

[Background] In contrast to Newton's well-known aphorism that he had been able “to see further only by standing on the shoulders of giants,” one attributes to the Spanish philosopher Ortega y Gasset the hypothesis saying that top-level research cannot be successful without a mass of medium researchers on which the top rests comparable to an iceberg. [Methodology/Principal Findings] The Ortega hypothesis predicts that highly-cited papers and medium-cited (or lowly-cited) papers would equally refer to papers with a medium impact. The Newton hypothesis would be supported if the top-level research more frequently cites previously highly-cited work than that medium-level research cites highly-cited work. Our analysis is based on (i) all articles and proceedings papers which were published in 2003 in the life sciences, health sciences, physical sciences, and social sciences, and (ii) all articles and proceeding papers which were cited within these publications. The results show that highly-cited work in all scientific fields more frequently cites previously highly-cited papers than that medium-cited work cites highly-cited work. [Conclusions/Significance] We demonstrate that papers contributing to the scientific progress in a field lean to a larger extent on previously important contributions than papers contributing little. These findings support the Newton hypothesis and call into question the Ortega hypothesis (given our usage of citation counts as a proxy for impact)