1.

Record Nr.

UNINA9910495887603321

Autore

Norton David L.

Titolo

Democracy and Moral Development : A Politics of Virtue / / David L. Norton

Pubbl/distr/stampa

Berkeley, California : , : University of California Press, , [1991]

©1991

ISBN

0-520-91721-9

0-585-03179-7

Edizione

[First edition.]

Descrizione fisica

1 online resource (xvi, 198 p. )

Disciplina

172

Soggetti

Political ethics - History

Ethics, Comparative

Ethics - Greece

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Includes index.

Nota di bibliografia

Includes bibliographical references (p. 177-192) and index.

Nota di contenuto

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

Sommario/riassunto

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.



2.

Record Nr.

UNINA9910254835903321

Titolo

Big Data Factories : Collaborative Approaches / / edited by Sorin Adam Matei, Nicolas Jullien, Sean P. Goggins

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017

ISBN

3-319-59186-X

Edizione

[1st ed. 2017.]

Descrizione fisica

1 online resource (VI, 141 p. 18 illus., 14 illus. in color.)

Collana

Computational Social Sciences, , 2509-9582

Disciplina

005.7

Soggetti

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

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references at the end of each chapters and index.

Nota di contenuto

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



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.

Sommario/riassunto

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.