The relation between Eigenfactor, audience factor, and influence weight

We present a theoretical and empirical analysis of a number of bibliometric indicators of journal performance. We focus on three indicators in particular, namely the Eigenfactor indicator, the audience factor, and the influence weight indicator. Our main finding is that the last two indicators can be regarded as a kind of special cases of the first indicator. We also find that the three indicators can be nicely characterized in terms of two properties. We refer to these properties as the property of insensitivity to field differences and the property of insensitivity to insignificant journals. The empirical results that we present illustrate our theoretical findings. We also show empirically that the differences between various indicators of journal performance are quite substantial

Publication patterns of award-winning forest scientists and implications for the ERA journal ranking

Publication patterns of 79 forest scientists awarded major international forestry prizes during 1990-2010 were compared with the journal classification and ranking promoted as part of the 'Excellence in Research for Australia' (ERA) by the Australian Research Council. The data revealed that these scientists exhibited an elite publication performance during the decade before and two decades following their first major award. An analysis of their 1703 articles in 431 journals revealed substantial differences between the journal choices of these elite scientists and the ERA classification and ranking of journals. Implications from these findings are that additional cross-classifications should be added for many journals, and there should be an adjustment to the ranking of several journals relevant to the ERA Field of Research classified as 0705 Forestry Sciences.Comment: 12 pages, 4 figures, 3 tables, 49 references; Journal of Informetrics (2011

A Review of Theory and Practice in Scientometrics

Scientometrics is the study of the quantitative aspects of the process of science as a communication system. It is centrally, but not only, concerned with the analysis of citations in the academic literature. In recent years it has come to play a major role in the measurement and evaluation of research performance. In this review we consider: the historical development of scientometrics, sources of citation data, citation metrics and the “laws" of scientometrics, normalisation, journal impact factors and other journal metrics, visualising and mapping science, evaluation and policy, and future developments

A review of the literature on citation impact indicators

Citation impact indicators nowadays play an important role in research evaluation, and consequently these indicators have received a lot of attention in the bibliometric and scientometric literature. This paper provides an in-depth review of the literature on citation impact indicators. First, an overview is given of the literature on bibliographic databases that can be used to calculate citation impact indicators (Web of Science, Scopus, and Google Scholar). Next, selected topics in the literature on citation impact indicators are reviewed in detail. The first topic is the selection of publications and citations to be included in the calculation of citation impact indicators. The second topic is the normalization of citation impact indicators, in particular normalization for field differences. Counting methods for dealing with co-authored publications are the third topic, and citation impact indicators for journals are the last topic. The paper concludes by offering some recommendations for future research

Bibliometric indicators and core journals in physical and rehabilitation medicine.

Background and objective: the concept of the "standing" of scientific journals (in terms of influence, prestige, popular ity, etc.) is multi-dimensional and cannot be captured adequately by a single indicator. The aim of this report is to compare and comment on different bibliometric indicators related to some leading journals in rehabilitation, in order to provide further insights regarding their practical usefulness for Physical and Rehabilitation Medicine. Discussion: The commonly used Journal Impact Factor and the new SCImago Journal Rank indicator are measures of average "impact per paper". Other new measures show potentially useful complementarities with them and warrant further attention. For example, the Eigenfactor score represents a measure of total "citation impact" and seems sufficiently to express the "importance" of a journal. In fact, the information conveyed by the Eigenfactor score corresponds to a general consensus of journal status in Physical and Rehabilitation Medicine, as expressed by the European Consensus Committee on "International Rehabilitation Journals" and captured by a survey among European Physical and Rehabilitation Medicine researchers

A recursive field-normalized bibliometric performance indicator: An application to the field of library and information science

Two commonly used ideas in the development of citation-based research performance indicators are the idea of normalizing citation counts based on a field classification scheme and the idea of recursive citation weighing (like in PageRank-inspired indicators). We combine these two ideas in a single indicator, referred to as the recursive mean normalized citation score indicator, and we study the validity of this indicator. Our empirical analysis shows that the proposed indicator is highly sensitive to the field classification scheme that is used. The indicator also has a strong tendency to reinforce biases caused by the classification scheme. Based on these observations, we advise against the use of indicators in which the idea of normalization based on a field classification scheme and the idea of recursive citation weighing are combined

Integrative Approach to Quality Assessment of Medical Journals Using Impact Factor, Eigenfactor, and Article Influence Scores

BACKGROUND: Impact factor (IF) is a commonly used surrogate for assessing the scientific quality of journals and articles. There is growing discontent in the medical community with the use of this quality assessment tool because of its many inherent limitations. To help address such concerns, Eigenfactor (ES) and Article Influence scores (AIS) have been devised to assess scientific impact of journals. The principal aim was to compare the temporal trends in IF, ES, and AIS on the rank order of leading medical journals over time. METHODS: The 2001 to 2008 IF, ES, AIS, and number of citable items (CI) of 35 leading medical journals were collected from the Institute of Scientific Information (ISI) and the http://www.eigenfactor.org databases. The journals were ranked based on the published 2008 ES, AIS, and IF scores. Temporal score trends and variations were analyzed. RESULTS: In general, the AIS and IF values provided similar rank orders. Using ES values resulted in large changes in the rank orders with higher ranking being assigned to journals that publish a large volume of articles. Since 2001, the IF and AIS of most journals increased significantly; however the ES increased in only 51% of the journals in the analysis. Conversely, 26% of journals experienced a downward trend in their ES, while the rest experienced no significant changes (23%). This discordance between temporal trends in IF and ES was largely driven by temporal changes in the number of CI published by the journals. CONCLUSION: The rank order of medical journals changes depending on whether IF, AIS or ES is used. All of these metrics are sensitive to the number of citable items published by journals. Consumers should thus consider all of these metrics rather than just IF alone in assessing the influence and importance of medical journals in their respective disciplines

An Assessment Tool for Academic Research Managers in the Third World

The academic evaluation of the publication record of researchers is relevant for identifying talented candidates for promotion and funding. A key tool for this is the use of the indexes provided by Web of Science and SCOPUS, costly databases that sometimes exceed the possibilities of academic institutions in many parts of the world. We show here how the data in one of the bases can be used to infer the main index of the other one. Methods of data analysis used in Machine Learning allow us to select just a few of the hundreds of variables in a database, which later are used in a panel regression, yielding a good approximation to the main index in the other database. Since the information of SCOPUS can be freely scraped from the Web, this approach allows to infer for free the Impact Factor of publications, the main index used in research assessments around the globe.Comment: 31 pages, 10 tables, 13 figure