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on Sociology of Economics |
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Issue of 2014‒03‒15
four papers chosen by Jonas Holmström Swedish School of Economics and Business Administration |
| By: | Ferda, HALICIOGLU |
| Abstract: | This research note presents the research rankings of Turkish economists in the world using RePEc database as of January 2014. The long-run research ranking data running from 2003 to 2013 are utilized to identify the research success of Turkish economists working at universities and institutions all over the world. |
| Keywords: | Research Ranking, Economics, Turkey, World |
| JEL: | A1 A2 F0 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:54058&r=sog |
| By: | Nagaoka, Sadao; Owan, Hideo |
| Abstract: | This paper examines what drives author ordering in scientific research. We first discussa theoretical framework for the choice between alphabetical ordering and relative-contribution-based ordering and develop hypotheses, focusing on the nature of research, in particular, the importance of collaboration in the context of incomplete contract, the measurement cost of contribution-based ordering and the role of a principal investigator (PI). Our empirical examinations, based on the new large scale original scientists' surveys in the US and Japan, show the supporting results. In particular, an alphabetical ordering is more likely to be used when the research is theoretical and has less empirical component and when the team size is large and not co-located. The variation of research method goes a long way in explaining the variation of the use of alphabetic ordering across fields (mathematics and economics vs. the others) as well as its variation within a field. We also find that PI or Co-PIs are more likely to exist when the project uses more resources as well as when the team is more heterogeneous. Finally, we confirm that author ordering sends two signals in contribution based ordering, the first author for the largest research contribution and the last author for the PI ( or Co-PI). |
| Keywords: | alphabetical ordering, contribution, science, incomplete contract, help, principal investigator |
| JEL: | O32 D23 |
| Date: | 2014–02 |
| URL: | http://d.repec.org/n?u=RePEc:hit:iirwps:13-23&r=sog |
| By: | Chia-Lin Chang; Michael McAleer (University of Canterbury) |
| Abstract: | The paper analyses academic journal quality and research impact using quality weighted citations versus total citations, based on the widely-used Thomson Reuters ISI Web of Science citations database (ISI). A new Index of Citations Quality (ICQ) is presented, based on quality weighted citations. The new index is used to analyse the leading 500 journals in both the Sciences and Social Sciences using quantifiable Research Assessment Measures (RAMs) that are based on alternative transformations of citations. It is shown that ICQ is a useful additional measure to 2YIF and other well known RAMs for the purpose of evaluating the impact and quality, as well as ranking, of journals as it contains information that has very low correlations with the information contained in the well known RAMs for both the Sciences and Social Sciences. |
| Keywords: | Research assessment measures, Impact factors, Eigenfactor, Article Influence, Quality weighted citations, Total citations, Index of citations quality, Journal rankings |
| JEL: | C18 C81 Y10 |
| Date: | 2014–02–23 |
| URL: | http://d.repec.org/n?u=RePEc:cbt:econwp:14/08&r=sog |
| By: | Javier Ruiz-Castillo; Ludo Waltman |
| Abstract: | In this paper, we build a sequence of twelve independent classification systems by applying the WVE algorithmic methodology introduced in Waltman & Van Eck (2012) to a large Web of Science (WoS) dataset consisting of 3.6 million articles published in 2005-2008 in academic journals, and the citations they receive during a five-year citation window for each year in that period. The number of clusters in the WVE sequence ranges from 390 to 73,205 in granularity levels 1 to 12. This contrasts with the 236 subject categories in the WoS classification system. We investigate two questions. Firstly, what are the main characteristics of the twelve WVE classification systems? Secondly, consider the possibility of evaluating the citation impact of the 500 universities in the 2013 edition of the CWTS Leiden Ranking using the Mean Normalized Citation Score (MNCS) indicator. The question is: what are the consequences of using the WoS classification system or an appropriately selected subset of the WVE classification systems for the ranking of universities according to the MNCS indicator? The main conclusions are the following. Firstly, we recommend focusing on two granularity levels in the WVE sequence that have a percentage of articles in small clusters that is smaller than 1% of the total, and that clearly show a greater homogeneity than the WoS system while they still capture in an acceptable way the skewness of science across clusters. Secondly, there is a strong correlation between the university rankings obtained under the WoS system and most WVE granularity levels. This does not preclude the existence of substantial differences among individual universities when classification systems are compared from two points of view: ordinal re-rankings, and quantitative differences between MNCS values. |
| Date: | 2014–03 |
| URL: | http://d.repec.org/n?u=RePEc:cte:werepe:we1403&r=sog |