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This book deals with uncertainty and graphing in scientific discovery work from a social practice perspective. It is based on a 5-year ethnographic study in an advanced experimental biology laboratory. The book shows how, in discovery work where scientists do not initially know what to make of graphs, there is a great deal of uncertainty and scientists struggle in trying to make sense of what to make of graphs. Contrary to the belief that scientists have no problem “interpreting” graphs, the chapters in this book make clear that uncertainty about their research object is tied to uncertainty of the graphs. It may take scientists several years of struggle in their workplace before they find out just what their graphs are evidence of. Graphs turn out to stand to the entire research in a part/whole relation, where scientists not only need to be highly familiar with the context from which their data are extracted but also with the entire process by means of which the natural world comes to be transformed and represented in the graph. This has considerable implications for science, technology, engineering, and mathematics education at the secondary and tertiary level, as well as in vocational training. This book discusses and elaborates these implications.
This book argues that the ‘constructivist metaphor’ has become a self-appointed overriding concept that suppresses other modes of thinking about knowing and learning science. Yet there are questions about knowledge that constructivism cannot properly answer, such as how a cognitive structure can intentionally develop a formation that is more complex than itself; how a learner can aim at a learning objective that is, by definition, itself unknown; how we learn through pain, suffering, love or passion; and the role emotion and crises play in knowing and learning. In support of the hypothesis that passibility underlies cognition, readers are provided with a collation of empirical studies and phenomenological analyses of knowing and learning science—in schools, scientific laboratories and everyday life—all of which defy a constructivist explanation. The author argues that ‘passibility’ constitutes an essential factor in the development of consciousness, with a range of essential experiences that cannot be brought into the linguistic realm. His exploration is guided by concepts such as ‘otherness’, passion, passivity and undecidability, and concludes by resituating the construction metaphor to accord it its proper place in a more comprehensive theory of learning.
This edited volume provides theoretical and practical resources relating to the ‘STEPWISE’ curricular and instructional framework. ‘STEPWISE’ is the acronym for Science & Technology Education Promoting Wellbeing for Individuals, Societies & Environments. It is a framework for organizing teaching and learning domains in ways that prioritize personal and social actions to address ‘critical socioscientific issues’ — that is, controversial decisions by powerful individuals/groups about science and technology (and related fields) that may adversely affect individuals, societies and/or environments. The book contains chapters written by and/or with teachers who have used STEPWISE to guide their instructional practices, as well as chapters written by education scholars who have used a range of theoretical lenses to analyze and evaluate STEPWISE — and, in several cases, described ways in which it relates to (or could relate to) their practices and/or ways in which the framework might logically be amended. Overall, this book offers educators, policy makers and others with resources useful for arranging science and technology education in ways that may assist societies in addressing significant potential personal, social and/or environmental problems — such as dramatic climate change, preventable human diseases, species losses, and social injustices — associated with fields of science and technology.
Since the emergence of the formal concept of probability theory in the seventeenth century, uncertainty has been perceived solely in terms of probability theory. However, this apparently unique link between uncertainty and probability theory has come under investigation a few decades back. Uncertainties are nowadays accepted to be of various kinds. Uncertainty in general could refer to different sense like not certainly known, questionable, problematic, vague, not definite or determined, ambiguous, liable to change, not reliable. In Indian languages, particularly in Sanskrit-based languages, there are other higher levels of uncertainties. It has been shown that several mathematical concepts such as the theory of fuzzy sets, theory of rough sets, evidence theory, possibility theory, theory of complex systems and complex network, theory of fuzzy measures and uncertainty theory can also successfully model uncertainty.
This book constitutes revised selected papers from the 8th and 9th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2018, held in Seville, Spain, on December 13–14, 2018, and SIMPDA 2019, held in Bled, Slovenia, on September 8, 2019. From 16 submissions received for SIMPDA 2018 and 9 submissions received for SIMPDA 2019, 3 papers each were carefully reviewed and selected for presentation in this volume. They cover theoretical issues related to process representation, discovery, and analysis or provide practical and operational examples of their application.
Cultural-historical activity theory frequently is used as a framework for studying static situations statically. In this book, the authors implement Lev Vygotsky’s call for doing unit rather than element analysis by studying activity dynamically, across different spatial and temporal scales. The eternal return, that is, the continual production of change while reproducing the system, is taken as the central metaphor for a system that produces self-movement. A case study is provided of salmon enhancement in British Columbia (Canada), linking the 120-year cultural history of this activity, with the 30-year evolution of a fish hatchery that concretely constitutes the system in one of the possible ways, and the knowing and learning of individual fish culturists, which is analyzed at the time scales of five years down to the micro-evolution of individual conversations. Most importantly, the authors implement Vygotsky’s call for theorizing affect and emotion at the very heart of the activity system, showing how the eternal return allows us to under-stand the change of worker involvement and identification with the goals of their workplace.
From the publisher. This book explores how graphs can serve as maps to guide us when the information we have is ambiguous or incomplete. Using a visually diverse sampling of graphical display, from heartrending autobiographical displays of genocide in the Kovno ghetto to the "Pie Chart of Mystery" in a New Yorker cartoon, Wainer illustrates the many ways graphs can be used--and misused--as we try to make sense of an uncertain world. Picturing the Uncertain World takes readers on an extraordinary graphical adventure, revealing how the visual communication of data offers answers to vexing questions yet also highlights the measure of uncertainty in almost everything we do. Are cancer rates higher or lower in rural communities? How can you know how much money to sock away for retirement when you don't know when you'll die? And where exactly did nineteenth-century novelists get their ideas? These are some of the fascinating questions Wainer invites readers to consider. Along the way he traces the origins and development of graphical display, from William Playfair, who pioneered the use of graphs in the eighteenth century, to instances today where the public has been misled through poorly designed graphs.
The 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25–28, 2023. The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.
The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: ​Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: ​Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: ​Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: ​Robustness; Time Series; Transfer and Multitask Learning. Part VI: ​Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. ​Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.
Reliable Knowledge Discovery focuses on theory, methods, and techniques for RKDD, a new sub-field of KDD. It studies the theory and methods to assure the reliability and trustworthiness of discovered knowledge and to maintain the stability and consistency of knowledge discovery processes. RKDD has a broad spectrum of applications, especially in critical domains like medicine, finance, and military. Reliable Knowledge Discovery also presents methods and techniques for designing robust knowledge-discovery processes. Approaches to assessing the reliability of the discovered knowledge are introduced. Particular attention is paid to methods for reliable feature selection, reliable graph discovery, reliable classification, and stream mining. Estimating the data trustworthiness is covered in this volume as well. Case studies are provided in many chapters. Reliable Knowledge Discovery is designed for researchers and advanced-level students focused on computer science and electrical engineering as a secondary text or reference. Professionals working in this related field and KDD application developers will also find this book useful.