Birger Hjørland

A citation index is a bibliographic database that provides citation links between documents. The first modern citation index was suggested by the researcher Eugene Garfield in 1955 and created by him in 1964, and it represents an important innovation to knowledge organization and information retrieval. This article describes citation indexes in general, considering the modern citation indexes, including Web of Science, Scopus, Google Scholar, Microsoft Academic, Crossref, Dimensions and some special citation indexes and predecessors to the modern citation index like Shepard’s Citations. We present comparative studies of the major ones and survey theoretical problems related to the role of citation indexes as subject access points (SAP), recognizing the implications to knowledge organization and information retrieval. Finally, studies on citation behavior are presented and the influence of citation indexes on knowledge organization, information retrieval and the scientific information ecosystem is recognized.

This chapter is going to address some issues about information science (IS) or library and information science (LIS) as an academic field. The main purpose is to set up a number of interrelated dimensions or levels, which are perhaps often taken for granted, but are here considered to be in need for systematic consideration. Each level is discussed in a separate section: 2. Is LIS an academic discipline? 3. What are the various names given to the field? How do they reflect underlying conceptions of the field? 4. What is the aim of LIS? What are we trying to achieve? 5. What are the core concepts of information science? 6. What are the most important metatheories and research traditions in the LIS?

There have been several calls from LIS researchers for practical or applied research not to ignore the epistemological assumptions underlying the systems and artifacts they design lest they showcase only the dominant theory at a given time. Others have also deplored the "epistemological promiscuity" or "eclecticism" of the field, its incessant borrowing of theories and models from elsewhere and the fact that the field has largely neglected the contributions that philosophy and epistemology could have made in its research. This problem raises that of the boundaries of LIS and is all the more troublesome because boundaries between epistemological theories are fuzzy. Indeed, some epistemological theories share the same basic assumptions or are historically derived from one another. Gathering a wide array of acknowledged theorists in philosophy of science and epistemology, this panel aims to examine how research work in the LIS field can clearly articulate the epistemological assumptions underlying that research and under what constraints this can be achieved. The topic is of prime importance to Information studies as a whole and to ASIST as the scientific flag bearer of information scientists worldwide.

This article is the first in a trilogy about the concept “science”. Section 1 considers the historical development of the meaning of the term science and shows its close relation to the terms “knowl­edge” and “philosophy”. Section 2 presents four historic phases in the basic conceptualizations of science science as representing absolute certain of knowl­edge based on deductive proof; science as representing absolute certain of knowl­edge based on “the scientific method”; science as representing fallible knowl­edge based on “the scientific method”; science without a belief in “the scientific method” as constitutive, hence the question about the nature of science becomes dramatic. Section 3 presents four basic understandings of the scientific method: Rationalism, which gives priority to a priori thinking; empiricism, which gives priority to the collection, description, and processing of data in a neutral way; historicism, which gives priority to the interpretation of data in the light of “paradigm” and pragmatism, which emphasizes the analysis of the purposes, consequences, and the interests of knowl­edge. The second article in the trilogy focus on different fields studying science, while the final article presets further developments in the concept of science and the general conclusion. Overall, the trilogy illuminates the most important tensions in different conceptualizations of science and argues for the role of information science and knowl­edge organization in the study of science and suggests how “science” should be understood as an object of research in these fields.

The basic realist claim is that a mind-independent reality exists. It should be common sense knowledge to accept this claim, just as any theories that try to deny it soon become inconsistent because reality strikes back. In spite of this, antirealist philosophies flourish, not only in philosophy but also in the behavioral and cognitive sciences and in information science. This is highly problematic because it removes the attention from reality to subjective phenomena with no real explanatory power. Realism should not be confused with the view that all scientific claims are true or with any other kind of naiveté concerning knowledge claims. The opposite of realism may be termed antirealism, idealism, or nominalism. Although many people confuse empiricism and positivism with realism, these traditions are by nature strongly antirealist, which is why a sharp distinction should be made between empiricism and realism. Empirical research should not be founded on assumptions about “the given” of observations, but should recognize the theory-laden nature of observations. Domain analysis represents an attempt to reintroduce a realist perspective in library and information science. A realist conception of relevance, information seeking, information retrieval, and knowledge organization is outlined. Information systems of all kinds, including research libraries and public libraries, should be informed by a realist philosophy and a realist information science.

This article presents and discusses different kinds of data documents, including data sets, data studies, data papers and data journals. It provides descriptive and bibliometric data on different kinds of data documents and discusses the theoretical and philosophical problems by classifying documents according to the DIKW model (data documents, information documents, knowl­edge documents and wisdom documents). Data documents are, on the one hand, an established category today, even with its own data citation index (DCI). On the other hand, data documents have blurred boundaries in relation to other kinds of documents and seem sometimes to be understood from the problematic philosophical assumption that a datum can be understood as “a single, fixed truth, valid for everyone, everywhere, at all times”