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Abstract

During the past decades, journal impact data obtained from the Journal Citation Reports (JCR) have gained relevance in library management, research management and research evaluation. Hence, both information scientists and bibliometricians share the responsibility towards the users of the JCR to analyse the reliability and validity of its measures thoroughly, to indicate pitfalls and to suggest possible improvements. In this article, ageing patterns are examined in 'formal' use or impact of all scientific journals processed for the Science Citation Index (SCI) during 1981-1995. A new classification system of journals in terms of their ageing characteristics is introduced. This system has been applied to as many as 3,098 journals covered by the Science Citation Index. Following an earlier suggestion by Glnzel and Schoepflin, a maturing and a decline phase are distinguished. From an analysis across all subfields it has been concluded that ageing characteristics are primarily specific to the individual journal rather than to the subfield, while the distribution of journals in terms of slowly or rapidly maturing or declining types is specific to the subfield. It is shown that the cited half life (CHL), printed in the JCR, is an inappropriate measure of decline of journal impact. Following earlier work by Line and others, a more adequate parameter of decline is calculated taking into account the size of annual volumes during a range of fifteen years. For 76 per cent of SCI journals the relative difference between this new parameter and the ISI CHL exceeds 5 per cent. The current JCR journal impact factor is proven to be biased towards journals revealing a rapid maturing and decline in impact. Therefore, a longer term impact factor is proposed, as well as a normalised impact statistic, taking into account citation characteristics of the research subfield covered by a journal and the type of documents published in it. When these new measures are combined with the proposed ageing classification system, they provide a significantly improved picture of a journal's impact to that obtained from the JCR.

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Footnote

Vgl. auch: http://www.bl.uk/information/finrap3.html

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Abstract

Over the past three years, the Dublin Core Metadata Initiative has achieved a broad international consensus on the semantics of a simple element set for describing electronic resources. Since the first workshop in March 1995, which was reported in the very first issue of D-Lib Magazine, Dublin Core has been the topic of perhaps a dozen articles here. Originally intended to be simple and intuitive enough for authors to tag Web pages without special training, Dublin Core is being adapted now for more specialized uses, from government information and legal deposit to museum informatics and electronic commerce. To meet such specialized requirements, Dublin Core can be customized with additional elements or qualifiers. However, these refinements can compromise interoperability across applications. There are tradeoffs between using specific terms that precisely meet local needs versus general terms that are understood more widely. We can better understand this inevitable tension between simplicity and complexity if we recognize that metadata is a form of human language. With Dublin Core, as with a natural language, people are inclined to stretch definitions, make general terms more specific, specific terms more general, misunderstand intended meanings, and coin new terms. One goal of this paper, therefore, will be to examine the experience of some related ways to seek semantic interoperability through simplicity: planned languages, interlingua constructs, and pidgins. The problem of semantic interoperability is compounded when we consider Dublin Core in translation. All of the workshops, documents, mailing lists, user guides, and working group outputs of the Dublin Core Initiative have been in English. But in many countries and for many applications, people need a metadata standard in their own language. In principle, the broad elements of Dublin Core can be defined equally well in Bulgarian or Hindi. Since Dublin Core is a controlled standard, however, any parallel definitions need to be kept in sync as the standard evolves. Another goal of the paper, then, will be to define the conceptual and organizational problem of maintaining a metadata standard in multiple languages. In addition to a name and definition, which are meant for human consumption, each Dublin Core element has a label, or indexing token, meant for harvesting by search engines. For practical reasons, these machine-readable tokens are English-looking strings such as Creator and Subject (just as HTML tags are called HEAD, BODY, or TITLE). These tokens, which are shared by Dublin Cores in every language, ensure that metadata fields created in any particular language are indexed together across repositories. As symbols of underlying universal semantics, these tokens form the basis of semantic interoperability among the multiple Dublin Cores. As long as we limit ourselves to sharing these indexing tokens among exact translations of a simple set of fifteen broad elements, the definitions of which fit easily onto two pages, the problem of Dublin Core in multiple languages is straightforward. But nothing having to do with human language is ever so simple. Just as speakers of various languages must learn the language of Dublin Core in their own tongues, we must find the right words to talk about a metadata language that is expressable in many discipline-specific jargons and natural languages and that inevitably will evolve and change over time.