Social Data Science Xennials - Campagnolo, Gian Marco

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Résumé :
This book explores the tension between analogue and digital as part of an evolving research programme and focuses on the sequencing of methods within it. The book will be an invaluable reference for scholars who routinely engage in critical sociological analysis of the digital workplace and find it easier to treat the digital as an object of study. It describes how the transformations taking place in the 10-year arc of a career spent doing fieldwork in the IT sector led the author to progressively embrace new forms of data and methods. In a time where sociological imagination takes the shape of whatever new phenomenon can be studied by transactional data and machine learning methods, it is a reminder that longstanding engagement with a particular field of practice is the basis of empirical social science expertise. 'This short book by Gian Marco Campagnolo is remarkably wide-ranging. It draws on theoretical perspectives as varied as Harold Garfinkel's ethnomethodologyand Andrew Abbott's 'linked ecologies' to discuss topics as diverse as the adoption of packaged enterprise software in the public sector in Italy and the careers of often influential industry analysts. Campagnolo's methods are primarily qualitative and ethnographic, but he shows a proper appreciation for quantitative methods such as text mining and sequence analysis. The book ends with a discussion of the famously difficult issue of achieving 'explainability' in machine learning. Campagnolo tantalisingly suggests the usefulness here of how ethnomethodologists view 'accountability': as a practical accomplishment that is hampered, rather than fostered, by efforts to give full explanations.' -Donald MacKenzie, Professor of Sociology, Edinburgh University, Scotland'The author adopts a 'processual' perspective on social data science as means of exploring and reflecting on the emergence of an academic career within this new domain of interdisciplinary inquiry. This is certainly a novel and interesting approach given the fact that 'data science' is work in progress and is characterized by a number of competing occupational groups that are struggling to define this emerging field.' -William Housley, Professor, University of Cardiff, UK 'Having myself written about the relationships between ethnography and computer science, I see this book as a timely contribution in that it extends the existing debate to data science. Data science is an emerging discipline that is gaining central stage in industry and in the public discourse. The aim of this book to indicate the importance of interdisciplinarity in this field is commendable.' -Giolo Fele, Professor, University of Trento, Italy 'This book provides two entwined accounts: a reflective personal journey across different projects and methods and a grounded, genealogically sound analysis of the approaches and contributions of social science to understanding the digital society. These dual accounts are adroitly communicated. Their bold combination yields a unique and invaluable contribution to fundamental discussions in the social sciences, as well as an exemplar for how to combine ethnographic and data-driven analysis in a theoretically and epistemologically informed manner. With this book, Campagnolo brings us close to the methods and opens up an inspiring and challenging agenda for combining old and new forms of inquiry into sociological problems.' -Anne Beaulieu, Director Data Research Centre, University of Groningen, Netherlands...

Biographie:
Gian Marco Campagnolo researches in the field of the social study of data science. He is Lecturer in Science, Technology and Innovation Studies at the University of Edinburgh and Faculty Fellow at the Alan Turing Institute, UK. He has previously had visiting appointments at the Copenhagen Centre for Social Data Science and the Centre de Sociologie de l'Innovation at the Ecole des Mines....

Sommaire:

Chapter 1 - Social Data Science Xennials

Chapter 2 - Phenomenological Extensions

Chapter 3 - The Analogue Mapping

Chapter 4 - Participative Epistemology

Chapter 5 - Ethnography as Data Science