Jack Andersen

Bio: Jack Andersen is an academic researcher from University of Copenhagen. The author has contributed to research in topics: Knowledge organization & Body of knowledge. The author has an hindex of 14, co-authored 38 publications receiving 601 citations.

What Genre Theory Does

Abstract: Purpose To provide a small overview of genre theory and its associated concepts and to show how genre theory has had its antecedents in certain parts of the social sciences and not in the humanities. Findings The chapter argues that the explanatory force of genre theory may be explained with its emphasis on everyday genres, de facto genres. Originality/value By providing an overview of genre theory, the chapter demonstrates the wealth and richness of forms of explanations in genre theory.

The Organization of Knowledge: Caught between Global Structures and Local Meaning

Abstract: The purpose of the chapter is to argue for a twofold understanding of knowledge organization: the organization of knowledge as a form of communicative action in digital culture and the organization of knowledge as an analytical means to address features of digital culture. The approach taken is an interpretative text-based form of argumentation. The chapter suggests that by putting forward such a twofold understanding of knowledge organization, new directions are given as to how to situate and understand the activity and practice of the organization of knowledge in digital culture. By offering the twofold understanding of the organization of knowledge, a tool of reflection is provided when users and the public at large try to make sense of, for example, data, archives, search engines, or algorithms. The originality of the chapter is its demonstration of how to conceive of knowledge organization as a form of communicative action and as an analytical means for understanding issues in digital culture.

Libraries as an infrastructure for a sustainable public sphere in a digital age

Abstract: This session will focus upon challenges to upholding a sustainable public sphere in a digital age and the potential of libraries to contribute to an infrastructure that might help us cope with these challenges. The workshop can be seen as a continuation of last year’s workshop themed Partnership with society: A social and cultural approach to iSchool research

Machine learning

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.).

The archaeology of knowledge

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.