The publication credit allocation problem is one of the fundamental problems in bibliometrics. There are two solutions which do not use any additional information: equal weights measure and the Shapley value. The paper justifies the equal weights measure by showing equivalence with the Shapley value approach for sharing co-authors performance in specific games.
We use data on economic, management and political science journals to produce quan- titative estimates of (in)consistency of the evaluations based on six popular bibliometric indicators (impact factor, 5-year impact factor, immediacy index, article influence score, SNIP and SJR). We advocate a new approach to the aggregation of journal rankings. Since the rank aggregation is a multicriteria decision problem, ranking methods from social choice theory may solve it. We apply either a direct ranking method based on the majority rule (the Copeland rule, the Markovian method) or a sorting procedure based on a tournament solution, such as the uncovered set and the minimal externally stable set. We demonstrate that the aggregate rankings reduce the number of contradictions and represent the set of the single-indicator-based rankings better than any of the six rankings themselves.
We present two ways (instantaneous and cumulative) to transform bibliographic networks, using the works’ publication year, into corresponding temporal networks based on temporal quantities. We also show how to use the addition of temporal quantities to define interesting temporal properties of nodes, links and their groups thus providing an insight into evolution of bibliographic networks. Using the multiplication of temporal networks we obtain different derived temporal networks providing us with new views on studied networks. The proposed approach is illustrated with examples from the collection of bibliographic networks on peer review.
An informal notion of distinction between scholarly journals is deeply embedded in bibliometric practice. Distinctions can be viewed as an operationalization of statistical relationships between journals. Bibliometric distinction can be regarded as a relative concept parameterized by the Kolmogorov--Smirnov statistic used as a basis for determining similarity or difference of journals. Within this framework, a systematic study of the probability distribution of distinctions makes it easier to understand the structure of the current scholarly communication. Using the Wakeby distribution, we propose a statistical description of the ``distinction machine'' at the core of the journals' diversity. In this paper, empirical research is based on a dataset of 230 physics journals indexed in Scopus in 2010 to 2015. The ranking of physics journals is obtained by computing the stationary probabilities in terms of Markov chain using transition probabilities derived from the distinction distribution. We perform a clustering of the physics journals according to a similarity that represents the statistical indistinguishability between the journals. This study could help practitioners to make decisions based on a deep understanding of the structure of scholarly communication.