Bio: Jack Andersen is an academic researcher from University of Copenhagen. The author has contributed to research in topic(s): Knowledge organization & Body of knowledge. The author has an hindex of 14, co-authored 38 publication(s) receiving 601 citation(s).
01 Mar 2006-Journal of Documentation
TL;DR: To demonstrate how information‐literacy is to have knowledge about information sources and that searching and using them is determined by an insight into how knowledge is socially organized in society, the paper takes a point of departure in Habermas' theory of the public sphere.
Abstract: Purpose – To provide some theoretical considerations concerning information literacy so as to contribute to a theoretically informed point of departure for understanding information literacy and to argue that to be an information literate person is to have knowledge about information sources and that searching and using them is determined by an insight into how knowledge is socially organized in society.Design/methodology/approach – Using concepts from composition studies that deal with the question of what a writer needs to know in order to produce a text, the paper outlines some ideas and key concepts in order to show how these ideas and concepts are useful to our understanding of information literacy. To demonstrate how information‐literacy is to have knowledge about information sources and that searching and using them is determined by an insight into how knowledge is socially organized in society, the paper takes a point of departure in Habermas' theory of the public sphere.Findings – Concludes that ...
01 Jun 2003-Journal of Documentation
TL;DR: There is a need to bring this model for scientific and technical communication to the focus of information science research as well as to update and revise it.
Abstract: In 1971 UNISIST proposed a model for scientific and technical communication. This model has been widely cited and additional models have been added to the literature. There is a need to bring this model to the focus of information science (IS) research as well as to update and revise it. There are both empirical and theoretical reasons for this need. On the empirical side much has happened in the developments of electronic communication that needs to be considered. From a theoretical point of view the domain‐analytic view has proposed that differences between different disciplines and domains should be emphasised. The original model only considered scientific and technical communication as a whole. There is a need both to compare with the humanities and social sciences and to regard internal differences in the sciences. There are also other reasons to reconsider and modify this model today. Offers not only a descriptive model, but also a theoretical perspective from which information systems may be unders...
01 Jul 2006-The Library Quarterly
TL;DR: In this paper, the authors examine the discipline of knowledge organization by harnessing the theories of Michel Foucault and Jurgen Habermas, and provide a sociohistorical analysis and critique of knowledge organisation in order to point out how the discipline understands itself and how it is a de facto human activity.
Abstract: In this article, the authors examine the discipline of knowledge organization by harnessing the theories of Michel Foucault and Jurgen Habermas. The argument is that knowledge organization is not just a question of improved technology; as an academic discipline, it has to define and legitimize its relevance for society. The authors use the theories of Foucault and Habermas to provide a sociohistorical analysis and critique of knowledge organization in order to point out how the discipline understands itself and how it is a de facto human activity. The self‐understanding of the discipline is investigated through the case of knowledge organization in the Danish public libraries at the beginning of the twentieth century, using the theories of Foucault. The second part of the article deals with the correspondence between the organization of society and knowledge organization based on the concept of Habermas’s public sphere.
01 Mar 2004
TL;DR: In this article, the relationship between social organization and knowledge organization is analyzed on two levels: first, in terms of an examination of how communication technologies have shaped forms of social organization, and second, the role of knowledge organization in scholarly communication by means of how indexing reflects and responds to the rhetorical activities of scholarly articles.
Abstract: In this dissertation I analyze the relationship between social organization and knowledge organization. This analysis is carried out on two levels. The first level consists of three chapters each examining particular perspectives of the relationship. First, in terms of an examination of how communication technologies have shaped forms of social organization, I argue how knowledge organization is constituted by social organization. Second, I further situate knowledge organization in light of Jürgen Habermas’ theory of the public sphere and argue that this theory can be viewed as a fundamental model of knowledge organization. Third, by drawing on various theories of genre and activity systems, I underpin the connection between social organization and knowledge organization further by seeking to integrate these with knowledge organization. The second level examines the role of knowledge organization in scholarly communication by means of how indexing reflects and responds to the rhetorical activities of scholarly articles. I consider this as how knowledge organization can ascribe cognitive authority to documents. The texts are considered to constitute the mediating link between social organization and knowledge organization. I conclude that this relationship between social organization and knowledge organization must be understood and examined in order to fully account for the role knowledge organization in human activities based on document production and use such as scholarly communication.
01 Dec 1996-ACM Computing Surveys
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
01 Jan 2009
01 Sep 1989
TL;DR: We may not be able to make you love reading, but archaeology of knowledge will lead you to love reading starting from now as mentioned in this paper, and book is the window to open the new world.
Abstract: We may not be able to make you love reading, but archaeology of knowledge will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.
01 Jan 1991-Quarterly Journal of Speech
01 Dec 1994-Journal of Religion & Health