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Abstract

This article examines the impact of the Internet and the age distribution of research scholars on academic citation age with a mathematical model proposed by Barnett, Fink, and Debus (1989) and a revised model that incorporates information about the online environment and scholar age distribution. The modified model fits the data well, accounting for 99.6% of the variance for science citations and 99.8% for social science citations. The Internet's impact on the aging process of academic citations has been very small, accounting for only 0.1% for the social sciences and 0.8% for the sciences. Rather than resulting in the use of more recent citations, the Internet appears to have lengthened the average life of academic citations by 6 to 8 months. The aging of scholars seems to have a greater impact, accounting for 2.8% of the variance for the sciences and 0.9% for the social sciences. However, because the diffusion of the Internet and the aging of the professoriate are correlated over this time period, differentiating their effects is somewhat problematic.

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Abstract

Vaughn, and Shaw look at the relationship between traditional citation and Web citation (not hyperlinks but rather textual mentions of published papers). Using English language research journals in ISI's 2000 Journal Citation Report - Information and Library Science category - 1209 full length papers published in 1997 in 46 journals were identified. Each was searched in Social Science Citation Index and on the Web using Google phrase search by entering the title in quotation marks, and followed for distinction where necessary with sub-titles, author's names, and journal title words. After removing obvious false drops, the number of web sites was recorded for comparison with the SSCI counts. A second sample from 1992 was also collected for examination. There were a total of 16,371 web citations to the selected papers. The top and bottom ranked four journals were then examined and every third citation to every third paper was selected and classified as to source type, domain, and country of origin. Web counts are much higher than ISI citation counts. Of the 46 journals from 1997, 26 demonstrated a significant correlation between Web and traditional citation counts, and 11 of the 15 in the 1992 sample also showed significant correlation. Journal impact factor in 1998 and 1999 correlated significantly with average Web citations per journal in the 1997 data, but at a low level. Thirty percent of web citations come from other papers posted on the web, and 30percent from listings of web based bibliographic services, while twelve percent come from class reading lists. High web citation journals often have web accessible tables of content.

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Abstract

How do authors refer to Web-based information sources in their formal scientific publications? It is not yet weIl known how scientists and scholars actually include new types of information sources, available through the new media, in their published work. This article reports an a comparative study of the lists of references in 38 scientific journals in five different scientific and social scientific fields. The fields are sociology, library and information science, biochemistry and biotechnology, neuroscience, and the mathematics of computing. As is weIl known, references, citations, and hyperlinks play different roles in academic publishing and communication. Our study focuses an hyperlinks as attributes of references in formal scholarly publications. The study developed and applied a method to analyze the differential roles of publishing media in the analysis of scientific and scholarly literature references. The present secondary databases that include reference and citation data (the Web of Science) cannot be used for this type of research. By the automated processing and analysis of the full text of scientific and scholarly articles, we were able to extract the references and hyperlinks contained in these references in relation to other features of the scientific and scholarly literature. Our findings show that hyperlinking references are indeed, as expected, abundantly present in the formal literature. They also tend to cite more recent literature than the average reference. The large majority of the references are to Web instances of traditional scientific journals. Other types of Web-based information sources are less weIl represented in the lists of references, except in the case of pure e-journals. We conclude that this can be explained by taking the role of the publisher into account. Indeed, it seems that the shift from print-based to electronic publishing has created new roles for the publisher. By shaping the way scientific references are hyperlinking to other information sources, the publisher may have a large impact an the availability of scientific and scholarly information.

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Abstract

Webometrics, the quantitative study of Web-related phenomena, emerged from the realization that methods originally designed for bibliometric analysis of scientific journal article citation patterns could be applied to the Web, with commercial search engines providing the raw data. Almind and Ingwersen (1997) defined the field and gave it its name. Other pioneers included Rodriguez Gairin (1997) and Aguillo (1998). Larson (1996) undertook exploratory link structure analysis, as did Rousseau (1997). Webometrics encompasses research from fields beyond information science such as communication studies, statistical physics, and computer science. In this review we concentrate on link analysis, but also cover other aspects of webometrics, including Web log fle analysis. One theme that runs through this chapter is the messiness of Web data and the need for data cleansing heuristics. The uncontrolled Web creates numerous problems in the interpretation of results, for instance, from the automatic creation or replication of links. The loose connection between top-level domain specifications (e.g., com, edu, and org) and their actual content is also a frustrating problem. For example, many .com sites contain noncommercial content, although com is ostensibly the main commercial top-level domain. Indeed, a skeptical researcher could claim that obstacles of this kind are so great that all Web analyses lack value. As will be seen, one response to this view, a view shared by critics of evaluative bibliometrics, is to demonstrate that Web data correlate significantly with some non-Web data in order to prove that the Web data are not wholly random. A practical response has been to develop increasingly sophisticated data cleansing techniques and multiple data analysis methods.