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Undiscovered public knowledge is a relatively unstudied phenomenon, and the few extended examples that have been published are intradisciplinary. This paper presents the concept of ‘facet’ as an example of interdisciplinary undiscovered public knowledge. ‘Facets’ were central to the bibliographic classification theory of S.R. Ranganathan in India and to the behavioural research of L. Guttman in Israel. The term had the same meaning in both fields, and the concept was developed and exploited at about the same time in both, but two separate, unconnected literatures grew up around the term and its associated concepts. This paper examines the origins and parallel uses of the concept and the term in both fields as a case study of interdisciplinary knowledge that could have been, but was apparently not, discovered any time between the early 1950s and the present using simple, readily available information retrieval techniques.

To suggest that a theory of classification for information retrieval (IR), asked for by Spärck Jones in a 1970 paper, presupposes a full implementation of a pragmatic understanding. Part of the Journal of Documentation celebration, “60 years of the best in information research”.

Literature‐based conceptual analysis, taking Spärck Jones as its starting‐point. Analysis involves distinctions between “positivism” and “pragmatism” and “classical” versus Kuhnian understandings of concepts.

Classification, both manual and automatic, for retrieval benefits from drawing upon a combination of qualitative and quantitative techniques, a consideration of theories of meaning, and the adding of top‐down approaches to IR in which divisions of labour, domains, traditions, genres, document architectures etc. are included as analytical elements and in which specific IR algorithms are based on the examination of specific literatures. Introduces an example illustrating the consequences of a full implementation of a pragmatist understanding when handling homonyms.

Outlines how to classify from a pragmatic‐philosophical point of view.

Provides, emphasizing a pragmatic understanding, insights of importance to classification for retrieval, both manual and automatic.

This paper aims to respond to the 2005 paper by Hjørland and Nissen Pedersen by suggesting that an exhaustive and universal classification of the phenomena that scholars study, and the methods and theories they apply, is feasible. It seeks to argue that such a classification is critical for interdisciplinary scholarship.

The paper presents a literature‐based conceptual analysis, taking Hjørland and Nissen Pedersen as its starting point. Hjørland and Nissen Pedersen had identified several difficulties that would be encountered in developing such a classification; the paper suggests how each of these can be overcome. It also urges a deductive approach as complementary to the inductive approach recommended by Hjørland and Nissen Pedersen.

The paper finds that an exhaustive and universal classification of scholarly documents in terms of (at least) the phenomena that scholars study, and the theories and methods they apply, appears to be both possible and desirable.

The paper suggests how such a project can be begun. In particular it stresses the importance of classifying documents in terms of causal links between phenomena.

The paper links the information science, interdisciplinary, and study of science literatures, and suggests that the types of classification outlined above would be of great value to scientists/scholars, and that they are possible.

The purpose of this paper is to provide an answer to a critique put forward by Szostak against a paper written by the present author.

The paper is based on a literature‐based conceptual analysis based on Hjørland and Nissen Pedersen and Szostak. The main points in a core theory of classification are outlined and Szostak's criticism is examined and answered.

The paper demonstrates theoretical differences between the views adduced by Hjørland and Nissen Pedersen on the one side and by Szostak on the other.

Theoretical clarification is important for the future development of the field.

The paper should be seen as one among others developing an argument for a theoretical foundation of classification informed by the theory of knowledge.

This paper seeks to outline the central role of concepts in the knowledge universe, and the intertwining roles of works, instantiations, and documents. In particular the authors are interested in ontological and epistemological aspects of concepts and in the question to which extent there is a need for natural languages to link concepts to create meaningful patterns.

The authors describe the quest for the smallest elements of knowledge from a historical perspective. They focus on the metaphor of the universe of knowledge and its impact on classification and retrieval of concepts. They outline the major components of an elementary theory of knowledge interaction.

The paper outlines the major components of an elementary theory of knowledge interaction that is based on the structure of knowledge rather than on the content of documents, in which semantics becomes not a matter of synonymous concepts, but rather of coordinating knowledge structures. The evidence is derived from existing empirical research.

The paper shifts the bases for knowledge organization from a search for a universal order to an understanding of a universal structure within which many context‐dependent orders are possible.

The purpose of this paper is to present the initial relationship between the Classification Research Group (CRG) and the Center for Documentation and Communication Research (CDCR) and how this relationship changed between 1952 and 1970. The theory of normative behavior and its concepts of worldviews, social norms, social types, and information behavior are used to characterize the relationship between the small worlds of the two groups with the intent of understanding the gap between early classification research and information retrieval (IR) research.

This is a mixed method analysis of two groups as evidenced in published artifacts by and about their work. A thorough review of historical literature about the groups as well as their own published works was employed and an author co-citation analysis was used to characterize the conceptual similarities and differences of the two groups of researchers.

The CRG focused on fundamental principles to aid classification and retrieval of information. The CDCR were more inclined to develop practical methods of retrieval without benefit of good theoretical foundations. The CRG began it work under the contention that the general classification schemes at the time were inadequate for the developing IR mechanisms. The CDCR rejected the classification schemes of the times and focused on developing punch card mechanisms and processes that were generously funded by both government and corporate funding.

This paper provides a unique historical analysis of two groups of influential researchers in the field of library and information science.

The principles of the author's theory of classification are summarized, and the necessity of expressing true relations between concepts in a classification is stressed. The logical faults in existing classifications (especially U.D.C., Bliss, and Colon) are discussed in comparison. The psychological and logical bases of the author's theory are considered in greater detail than before, especially as regards the derivation of the operators. In this connexion a change has been found necessary in the writing of the reaction operator, being A/—B, for B acts on A (instead of A—/B). Four new operators are introduced, being ‘dimensional’ (time and space, &c.), ‘comparison’, ‘association’, and ‘concurrence’, the last three on a basis of learning theory and work on conditioned responses in psychology. Examples are given of their uses. Operators represent logical relations, and their meanings, in everyday language, are discussed. The selection of a preferred order for the construction of a classification is shown to be possible on a logical basis, being the fully deductive order. The problem of notation is then dealt with in detail. It is shown that a fully elastic ‘deductive’ notation, allowing extrapolation and interpolation in all ways, not achieved in other classifications, is possible, but still does not meet the requirements of inductive classification. A notation is developed which provides arbitrary symbols for isolates, connected by operator symbols, and this is shown to be the only solution which meets all the requirements for expressing an inductive classification according to the author's theory.

Stakeholder theory has been accused of being an umbrella concept rather than a distinct theory per se. Recognizing the stakeholder concept as an essentially contested concept subject to multiple competing interpretations, this chapter presents a systematic meta-level conceptual analysis. This chapter aims to contribute to the optimal development of stakeholder theory by clarifying the conceptual confusion surrounding its central construct to help prevent stakeholder theory from developing into an accumulation of disparate ideas. Multi-contextual contributions to stakeholder theory are analysed via an unparalleled bounded systematic review of 593 stakeholder definitions. Determinants of the stakeholder concept have been deconstructed and analysed to establish how definitional variables relate to variants of stakeholder theory. These determinants have been sorted, filtered and ordered to produce a comprehensive, multi-dimensional classification of stakeholder theory based on four hyponyms which relate to 16 definitional categories. The classification was then subjected to empirical testing with positive results. This evaluation of the stakeholder concept illustrates how contributions are aligned and interrelated, thereby prescribing what is acceptable (unacceptable) as inclusion within stakeholder theory. An invaluable overview of what we know about stakeholder theory is presented within a single model, drawing the conclusion that stakeholder theory is indeed a single theory.

The purpose of this paper is to explore the modernity of current classification theory and work, and outline a foundation for moving classification toward a late‐modern conception.

The paper examines the conceptual foundation for current modern classification work, provides critical analysis of that approach, and outlines three conflicts with modernity that shape the path out of the consequences of modernity.

The paper presents an understanding of classification that establishes classification on a late‐modern epistemology, and it lays the contours of how to reclaim the intellectual core of classification theory and work.

The paper establishes a foundation for rethinking classification work, outlines consequences of current mainstream work, and provides concept for developing late‐modern classification theory and practice.

The suggestion that classifications for retrieval should be constructed automatically raises some serious problems concerning the sorts of classification which are required, and the way in which formal classification theories should be exploited, given that a retrieval classification is required for a purpose. These difficulties have not been sufficiently considered, and the paper therefore attempts an analysis of them, though no solutions of immediate application can be suggested. Starting with the illustrative proposition that a polythetic, multiple, unordered classification is required in automatic thesaurus construction, this is considered in the context of classification in general, where eight sorts of classification can be distinguished, each covering a range of class definitions and class‐finding algorithms. The problem which follows is that since there is generally no natural or best classification of a set of objects as such, the evaluation of alternative classifications requires cither formal criteria of goodness of fit, or, if a classification is required for a purpose, a precise statement of that purpose. In any case a substantive theory of classification is needed, which does not exist; and since sufficiently precise specifications of retrieval requirements are also lacking, the only currently available approach to automatic classification experiments for information retrieval is to do enough of them.