Logic Programming Applications: What Are the Abstractions and Implementations?

This article presents an overview of applications of logic programming, classifying them based on the abstractions and implementations of logic languages that support the applications. The three key abstractions are join, recursion, and constraint. Their essential implementations are for-loops, fixed points, and backtracking, respectively. The corresponding kinds of applications are database queries, inductive analysis, and combinatorial search, respectively. We also discuss language extensions and programming paradigms, summarize example application problems by application areas, and touch on example systems that support variants of the abstractions with different implementations

Linguistics and LIS: A Research Agenda

Linguistics and Library and Information Science (LIS) are both interdisciplinary fields that draws from areas such as languages, psychology, sociology, cognitive science, computer science, anthropology, education, and management. The theories and methods of linguistic research can have significant explanatory power for LIS. This article presents a research agenda for LIS that proposes the use of linguistic analysis methods, including discourse analysis, typology, and genre theory

Towards a multi-actor theory of public value co-creation

This essay suggests changes to the theory of public value and, in particular, the strategic triangle framework, in order to adapt it to an emerging world where policy makers and managers in the public, private, voluntary and informal community sectors have to somehow separately and jointly create public value. One set of possible changes concerns what might be in the centre of the strategic triangle besides the public manager. Additional suggestions are made concerning how multiple actors, levels, arenas and/or spheres of action, and logics might be accommodated. Finally, possibilities are outlined for how the strategic triangle might be adapted to complex policy fields in which there are multiple, often conflicting organizations, interests and agendas. In other words, how might politics be more explicitly accommodated. The essay concludes with a number of research suggestions

EXPANDING UNDERSTANDING OF PUBLIC POLICY AS A COMPLEX AND PLURI-DISCIPLINARY SYSTEM: ILLUSTRATING POSSIBILITIES OF EPISTEMIC PLURALISM

Science, and especially the social sciences, has developed as distinct territories, each with its own vocabulary and language-game (Wittgenstein, 1945/1958). Yet understanding and explaining science as a complex and pluri-disciplinary system has important practitioner-oriented as well as research-oriented and other benefits, benefits that can be enhanced through the use of cloud-based technologies. Understanding and explaining public policy in particular as complex can inform and transform the way problems are approached. This is particularly important for an action subject like public policy and administration that can be considered as having been influenced by many disciplines. Public administration (PA) through multiple perspectives, already in the literature as epistemic pluralism (Farmer, 2010), aims to transform PA’s language-game by increasing the imaginative nature of knowledge. The practical application of epistemic pluralism has also been established. This dissertation further extends theory to practice by conceptualizing a cloud-based tool called Wittgenstein X. A cloud-based tool to organize and make sense of public policy and administration through multiple perspectives will provide a mechanism for researchers, practitioners, students, and others to increase the imaginative nature of knowledge. The application of EP theory and practice to big data will also be considered. This dissertation conceptualizes complexity theory as the fundamental vantage point from which science in general and public policy and administration in particular can be understood. It asks: What is the relevance of understanding and explaining public policy as a complex and pluri-disciplinary system and how is this related to big data? This study is important because it offers a remedy to resolving seemingly intractable problems in PPA. The component terms of this study, science, complexity, pluri-disciplinarity, systems, and governmentality will be shown as linked in a Wittgensteinian Family Resemblance. The terms can be said to merge into a whole where the whole is greater than the sum of the parts

The Sales Impact of Storytelling in Live Streaming E-Commerce

Live streaming e-commerce (LSE) has emerged as a popular third-party service for improving product sales. It persuades consumers through streamers’ storytelling or narratives, which encompass descriptions and depictions of their own product experiences. However, the sales impact of a story or narrative in LSEs has been overlooked in the literature. Extending the narrative transportation theory to the LSE context, we posit that the dual landscapes of narrative—the landscapes of action and the landscape of consciousness—can improve product sales through their influence on consumers’ imagination of story plotline and empathy for streamers’ product experiences. We also propose that the efficacy of the dual landscapes is contingent on streamers’ interaction response to consumer query. By collecting LSE data from the Taobao Live platform, we manually and algorithmically measured these variables and proposed to empirically examine their effects

Literacy policy and English/literacy practice: : Researching the interaction between different knowledge fields

This article considers the role of research in disentangling an increasingly complex relationship between literacy policy and practice as it is emerging in different local and national contexts. What are the tools and methodologies that have been used to track this relationship over time? Where should they best focus attention now? In answering these questions this paper will consider three different kinds of research perspectives and starting points for enquiry: 1. Policy evaluation. The use of a range of quantitative research tools to feed policy decision-making by tracking the impact on pupil performance of different kinds of pedagogic or policy change (Organisation for Economic Co-operation and Development [OECD], 2010). 2. Co-construction and policy translation. This has for some time been a central preoccupation in policy sociology, which has used small-scale and context specific research to test the limits to the control over complex social fields that policy exercises from afar (Ball, 1994). Agentic re-framings of policy at the local level stand as evidence for the potential to challenge, mitigate or reorder such impositions. 3. Ethnographies of policy time and space. Ethnographic research tools have long been used to document community literacy practices, and in training their lens on the classroom have sought to focus on the potential dissonance between community and schooled practices. It is rarer to find such research tools deployed to explore the broader policy landscape. In the light of debate within the field, part of the purpose of this article is to examine how ethnographic research tools might be refined to study how policy from afar reshapes literacy practices in the here and now. (Brandt and Clinton, 2002)

Conceptual Modeling in Law: An Interdisciplinary Research Agenda

The article describes how different approaches from the IS field of conceptual modeling should be transferred to the legal domain to enhance comprehensibility of legal regulations and contracts. It is further described how this in turn would benefit the IS discipline. The findings emphasize the importance of further interdisciplinary research on that topic. A research agenda that synthesizes the presented ideas is proposed based on a framework that structures the research field. Researchers from both disciplines, IS and Law, that are interested in this field should use the research agenda to position their research and to derive new and innovative research questions

Training IBM Watson Using Automatically Generated Question-Answer Pairs

IBM Watson is a cognitive computing system capable of question answering in natural languages. It is believed that IBM Watson can understand large corpora and answer relevant questions more effectively than any other question-answering system currently available. To unleash the full power of Watson, however, we need to train its instance with a large number of well-prepared question-answer pairs. Obviously, manually generating such pairs in a large quantity is prohibitively time consuming and significantly limits the efficiency of Watson’s training. Recently, a large-scale dataset of over 30 million question-answer pairs was reported. Under the assumption that using such an automatically generated dataset could relieve the burden of manual question-answer generation, we tried to use this dataset to train an instance of Watson and checked the training efficiency and accuracy. According to our experiments, using this auto-generated dataset was effective for training Watson, complementing manually crafted question-answer pairs. To the best of the authors’ knowledge, this work is the first attempt to use a large-scale dataset of automatically generated question-answer pairs for training IBM Watson. We anticipate that the insights and lessons obtained from our experiments will be useful for researchers who want to expedite Watson training leveraged by automatically generated question-answer pairs