With or without h-index? Comparing aggregates of rankings based on seven popular bibliometric indicators
We apply five majority-rule-based ordinal ranking methods to data on economic, management
and political science journals in order to produce aggregate journal rankings. First, we
calculate aggregates for the set of rankings based on seven popular bibliometric indicators
(impact factor, 5-year impact factor, immediacy index, article influence score, h-index, SNIP
and SJR). Then, we exclude the Hirsch index and repeat the calculations. We perform the
comparative correlation analysis of the aggregates and the initial rankings. We use two rank
measures of correlation, Kendall’s tau-b and the share of coinciding pairs r. The analysis
demonstrates that aggregate rankings represent the set of single-indicator-based rankings
better than any of the seven rankings themselves. Among the single-indicator-based rankings
themselves, the best representations of their set are produced by the 5-year impact factor. The
least representative are rankings based on the immediacy index. The exclusion of the Hirsch
index from the set of indicators does not change these results.
Academic rewards and honors are proven to correlate with h-index, although it was not the decision criterion for them till recent years. Once h-index becomes the rule-setting scientometric ranking measure in the zero-sum game for academic positions and research resources as suggested by its advocates, the rational behavior of competing academics is expected to converge towards its game- theoretic solution. This paper derives the game-theoretic solution, its evidence in scientometric data and discusses its consequences on the development of science. DBLP database of 07/2017 was used for mining. Additionally, the openly available scientometric datasets are introduced as a good alternative to commercial datasets of comparable size for public research in behavioral sciences.
This paper introduces a systematic technology trend monitoring (TTM) methodology based on an analysis of bibliometric data. Among the key premises for developing a methodology are: (1) the increasing number of data sources addressing different phases of the STI development, and thus requiring a more holistic and integrated analysis; (2) the need for more customized clustering approaches particularly for the purpose of identifying trends; and (3) augmenting the policy impact of trends through gathering future-oriented intelligence on emerging developments and potential disruptive changes. Thus, the TTM methodology developed combines and jointly analyzes different datasets to gain intelligence to cover different phases of the technological evolution starting from the ‘emergence’ of a technology towards ‘supporting’ and ‘solution’ applications and more ‘practical’ business and market-oriented uses. Furthermore, the study presents a new algorithm for data clustering in order to overcome the weaknesses of readily available clusterization tools for the purpose of identifying technology trends. The present study places the TTM activities into a wider policy context to make use of the outcomes for the purpose of Science, Technology and Innovation policy formulation, and R&D strategy making processes. The methodology developed is demonstrated in the domain of “semantic technologies”.
The 17th International Conference of the International Society for Scientometrics and Informetrics (ISSI2019) is held on 2-5 September 2019 in Rome, and is hosted by the Sapienza University of Rome. It is a major event with participants from 44 countries from all global regions.
The conference includes a special STI Indicators Conference Track organized in collaboration with the European Network of Indicator Designers (ENID). In this way, ISSI2019 represents a first experiment to bring together the two conferences in a particular year.
In a first round, around 420 submissions were made of full papers and research-in-progress papers. After an extensive peer review process, thoroughly conducted by over 200 reviewers, some 260 submissions were accepted for oral presentation, while the authors of another 80 submissions were invited to present their paper as a poster. Of these 80, about 60 per cent accepted this invitation. A second round of poster submissions was held, resulting in some 130 new poster submissions.
In the final acceptance decision, apart from the reviewer judgments, two additional rules were applied: for oral presentations of papers a one-presentation- per-presenter rule, and for papers and posters the rule that all contributions must be included in the conference proceedings. All in all, the Conference Proceedings contain 261 oral presentations of accepted papers, and 156 poster presentations.
Proceedings of the science and technology indicators conference 2018 Leiden.
Proceedings of the 21 International Conference on Science and Technology Indicators: Peripheries, frontiers and beyond.
14-16 September 2016
Universitat Politècnica de València
Abstract Clustering cities based on their socio-economic development in long time period is an important issue and may be used in many ways, e.g., in strategic regional planning. In this paper we continue our recent study where cumulative attribute for each year replaces nine other attributes, called ’vector of dynamics’. In our previous paper some original ranking method was proposed. Using the same data set, here we try out some classical clustering models such as Minimum sum of squares and Harmonic means clustering. Results for the two last models are obtained using Variable neighborhood search based heuristics. A comparative study among old and new results on 120 Russian large cities are provided and analyzed.
An analysis of journals’ rankings based on five commonly used bibliometric indicators (impact factor, article influence score, SNIP, SJR, and H-index) has been conducted. It is shown that despite the high correlation, these single-indicatorbased rankings are not identical. Therefore, new approach to ranking academic journals is proposed based on the aggregation of single bibliometric indicators using several ordinal aggregation procedures. In particular, we use the threshold procedure, which allows to reduce opportunities for manipulations.
At early 2016 the new index was launched on Web of Science platform — Russian Science Citation Index (RSCI). The database is free for all Web of Science subscribers except those from the former Soviet Union countries. This database includes publications from 652 best Russian journals and is based on the data from Russian national citation index — Russian Index of Science Citation (RISC). RISC was launched in 2005 but there is very limited information about it available in English-language scholarly literature by now. The aim of this paper is to describe the history, actual structure and user possibilities of RISC. We focus on the novel features of RISC which are crucial to bibliometrics and unavailable in international citation indices.