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1. |
Record Nr. |
UNINA9910495887603321 |
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Autore |
Norton David L. |
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Titolo |
Democracy and Moral Development : A Politics of Virtue / / David L. Norton |
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Pubbl/distr/stampa |
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Berkeley, California : , : University of California Press, , [1991] |
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©1991 |
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ISBN |
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0-520-91721-9 |
0-585-03179-7 |
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Edizione |
[First edition.] |
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Descrizione fisica |
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1 online resource (xvi, 198 p. ) |
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Disciplina |
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Soggetti |
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Political ethics - History |
Ethics, Comparative |
Ethics - Greece |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references (p. 177-192) and index. |
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Nota di contenuto |
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Front matter -- Contents -- Preface -- Acknowledgments -- Introduction -- CHAPTER ONE. Classical Liberalism: Individuality Recovered But Misdirected -- CHAPTER TWO. Individuality Reconceived and Redirected: The Doctrine of Developmental Democracy -- CHAPTER THREE. Implementation of Developmental Democracy: Focus Upon Adolescence -- CHAPTER FOUR. Meaningful Work to Meaningful Living: The Virtues and Politics -- CHAPTER FIVE. Responsibilities and Rights -- CHAPTER SIX. Community as the Sociality of True Individuals -- CHAPTER SEVEN. Good Government -- Notes -- Index |
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Sommario/riassunto |
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At a time when politics and virtue seem less compatible than oil and water, Democracy and Moral Development shows how to bring the two together. Philosopher David Norton applies classical concepts of virtue to the premises of modern democracy. The centerpiece of the book is a model of organizational management applicable to the state, business, the professions, and voluntary communities. |
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2. |
Record Nr. |
UNINA9910254835903321 |
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Titolo |
Big Data Factories : Collaborative Approaches / / edited by Sorin Adam Matei, Nicolas Jullien, Sean P. Goggins |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (VI, 141 p. 18 illus., 14 illus. in color.) |
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Collana |
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Computational Social Sciences, , 2509-9582 |
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Disciplina |
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Soggetti |
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Data mining |
Quantitative research |
Bioinformatics |
Social sciences - Data processing |
Science - Moral and ethical aspects |
Data Mining and Knowledge Discovery |
Data Analysis and Big Data |
Computer Application in Social and Behavioral Sciences |
Science Ethics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Chapter1. Introduction -- Part 1: Theoretical Principles and Approaches to Data Factories -- Chapter2. Accessibility and Flexibility: Two Organizing Principles for Big Data Collaboration -- Chapter3. The Open Community Data Exchange: Advancing Data Sharing and Discovery in Open Online Community Science -- Part 2: Theoretical principles and ideas for designing and deploying data factory approaches -- Chapter4. Levels of Trace Data for Social and Behavioral Science Research -- Chapter5. The 10 Adoption Drivers of Open Source Software that Enables e-Research in Data Factories for Open Innovations -- Chapter6. Aligning online social collaboration data around social order: theoretical considerations and measures -- Part 3: Approaches in action through case studies of data based research, best practice scenarios, or educational briefs -- Chapter7. Lessons learned |
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from a decade of FLOSS data collection -- Chapter8. Teaching Students How (NOT) to Lie, Manipulate, and Mislead with Information Visualizations -- Chapter9. Democratizing Data Science: The Community Data Science Workshops and Classes. |
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Sommario/riassunto |
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The book proposes a systematic approach to big data collection, documentation and development of analytic procedures that foster collaboration on a large scale. This approach, designated as “data factoring” emphasizes the need to think of each individual dataset developed by an individual project as part of a broader data ecosystem, easily accessible and exploitable by parties not directly involved with data collection and documentation. Furthermore, data factoring uses and encourages pre-analytic operations that add value to big data sets, especially recombining and repurposing. The book proposes a research-development agenda that can undergird an ideal data factory approach. Several programmatic chapters discuss specialized issues involved in data factoring (documentation, meta-data specification, building flexible, yet comprehensive data ontologies, usability issues involved in collaborative tools, etc.). The book also presents case studies for data factoring and processing that can lead to building better scientific collaboration and data sharing strategies and tools. Finally, the book presents the teaching utility of data factoring and the ethical and privacy concerns related to it. Chapter 9 of this book is available open access under a CC BY 4.0 license at link.springer.com. |
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