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Purpose: The purpose of this paper is to characterize library and information science (LIS) as fragmenting discipline both historically and by applying Whitley's (1984) theory about the organization of sciences and Fuchs' (1993) theory about scientific change. Design/methodology/approach: The study combines historical source analysis with conceptual and theoretical analysis for characterizing LIS. An attempt is made to empirically validate the distinction between LIS context, L&I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies. Findings: The origin of fragmentation in LIS due the contributions of other disciplines can be traced in the 1960s and 1970s for solving the problems produced by the growth of scientific literature. Computer science and business established academic programs and started research relevant to LIS community focusing on information retrieval and bibliometrics. This has led to differing research interests between LIS and other disciplines concerning research topics and methods. LIS has been characterized as fragmented adhocracy as a whole, but we make a distinction between research topics LIS context, L&I services and information seeking as fragmented adhocracies and information retrieval and scientific communication (scientometrics) as technologically integrated bureaucracies. Originality/value: The paper provides an elaborated historical perspective on the fragmentation of LIS in the pressure of other disciplines. It also characterizes LIS as discipline in a fresh way by applying Whitley's (1984) theory. [ABSTRACT FROM AUTHOR]

Purpose Information seeking is often performed in collaborative contexts. The research into such collaborative information seeking (CIS) has been proceeding since the 1990s but lacks methodological discussions. The purpose of this paper is to analyze and discuss methodological issues in existing CIS studies.Design/methodology/approach The authors systematically review 69 empirical CIS studies.Findings The review shows that the most common methods of data collection are lab experiments (43 percent), observation (19 percent) and surveys (16 percent), that the most common methods of data analysis are description (33 percent), statistical testing (29 percent) and content analysis (19 percent) and that CIS studies involve a fairly even mix of novice, intermediate and specialist participants. However, the authors also find that CIS research is dominated by exploratory studies, leaves it largely unexplored in what ways the findings of a study may be specific to the particular study setting, appears to assign primacy to precision at the expense of generalizability, struggles with investigating how CIS activities extend over time and provides data about behavior to a larger extent than about reasons, experiences and especially outcomes.Research limitations/implications The major implication of this review is its identification of the need for a shared model to which individual CIS studies can contribute in a cumulative manner. To support the development of such a model, the authors discuss a model of the core CIS process and a model of the factors that trigger CIS.Originality/value This study assesses the current state of CIS research, provides guidance for future CIS studies and aims to inspire further methodological discussion. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to introduce the concept of Dark Knowledge, an epistemology that acknowledges both alternative knowledge and ways of knowing which are cognizant of the moral and ethical positioning of each. Design/methodology/approach: This is a conceptual paper that uses existing relevant literature to develop the work. The paper uses a four-stage literature search process and draws upon a range of disciplines, including philosophy, computer science and information management, to underpin the evolution of the concept. Findings: As a conceptual paper, no empirical findings are presented. Instead, the paper presents an embryonic model of Dark Knowledge and identifies a number of characteristics, which may be used to explore the concept in more detail. Research limitations/implications: There is a clear need to develop a body of empirical work, adding to the theoretical perspectives presented in this paper. It is anticipated that this paper will provide one of the cornerstones for future studies in this area. Originality/value: The paper makes an original contribution to the study of information behaviours, practices and epistemology. [ABSTRACT FROM AUTHOR]

Purpose: To ascertain the diffusion and influence of Savolainen's ELIS model and its use as a theoretical and/or methodological basis for research. Design/methodology/approach: A context citation analysis was made of the work where this researcher published his model. Analysis covered the year of publication, the type of work and the subject matter of the citing documents concerned. In-context citations were analysed for their frequency in each citing text, style, location and content cited. Findings: The ELIS model received 18.5 cites/year. 20.2 per cent of them corresponded to papers published in journals in other areas, mainly computer science. The average of cites per paper was 1.8; 64.5 percent of the citing works cited them only once. 60 per cent of the cites were considered essential. Only 13.7 per cent of these cites appear in theory or methods. 37 per cent of the citing documents contained no concept relating to the model. Research limitations/implications: The method used focuses on the most direct context of a cite (sentence or paragraph), but isolates it from the general context (full document, other documents by the author or their social capital). It has, however, allowed this research issue to be dealt with under laboratory conditions and revealed nuances hidden by the absolute number of cites. Originality/value: It has become evident that the dissemination and influence of the ELIS model are less than what the total number of cites indicates and that it has scarcely been incorporated into research design. Despite its popularity, it is not being validated and/or refuted by way of empirical data. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to understand how typical users of YouTube judge the relevance of videos in leisure contexts; what are the reasons users give when judging video material as relevant or not relevant?Design/methodology/approach A naturalistic diary was performed in which 30 participants completed diaries providing details on their video relevance criteria. The analysis revealed 28 relevance criteria grouped into eight categories.Findings In total, 28 relevance criteria were identified through the analyses of the diaries’ content and they were grouped into eight categories. The findings revealed that criteria related to the content of the video are the most dominant group of criteria with topicality being the most dominant criterion. There is a considerable overlap between leisure relevance criteria and previous relevance criteria studies, but the importance of these criteria varies among different contexts. New criteria, e.g. habit emerged from the data which tend to be more related to leisure contexts.Research limitations/implications The decision to follow a naturalistic approach reduced the level of control on the study. A further limitation can be found in the participants’ sample used in this study, all the participants of the main study were university or college students.Practical implications This study attempted to enrich the current literature by investigating users’ video relevance criteria in leisure contexts. This investigation might have implications on the design of video search systems.Originality/value Previous relevance criteria studies focussed on work contexts and the information judged was mainly in text format. This paper outlines new insights by investigating video relevance criteria in leisure context. [ABSTRACT FROM AUTHOR]

Purpose ? To provide an integrated perspective to similarities and differences between approaches to automated classification in different research communities (machine learning, information retrieval and library science), and point to problems with the approaches and automated classification as such. Design/methodology/approach ? A range of works dealing with automated classification of full-text web documents are discussed. Explorations of individual approaches are given in the following sections: special features (description, differences, evaluation), application and characteristics of web pages. Findings ? Provides major similarities and differences between the three approaches: document pre-processing and utilization of web-specific document characteristics is common to all the approaches; major differences are in applied algorithms, employment or not of the vector space model and of controlled vocabularies. Problems of automated classification are recognized. Research limitations/implications ? The paper does not attempt to provide an exhaustive bibliography of related resources. Practical implications ? As an integrated overview of approaches from different research communities with application examples, it is very useful for students in library and information science and computer science, as well as for practitioners. Researchers from one community have the information on how similar tasks are conducted in different communities. Originality/value ? To the author's knowledge, no review paper on automated text classification attempted to discuss more than one community's approach from an integrated perspective. [ABSTRACT FROM AUTHOR]

The increasing availability of digital libraries with cross-citation data on the Internet enables new studies in bibliometrics. The paper focuses on the list of 10,000 top-cited authors in computer science available as part of CiteSeer. Using data from several consecutive lists a model of how authors accrue citations with time is constructed. By comparing the rate at which individual authors accrue citations with the average rate, predictions are made of how their ranking in the list will change in the future. [ABSTRACT FROM AUTHOR]