How Data Need People : The Social and Epistemic Practice of a Data-Rich Science

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¥26,475
  • 電子書籍
  • ポイントキャンペーン

How Data Need People : The Social and Epistemic Practice of a Data-Rich Science

  • 著者名:Hoeppe, Götz
  • 価格 ¥6,714 (本体¥6,104)
  • Cambridge University Press(2026/06/18発売)
  • 向夏の候!Kinoppy 電子書籍・電子洋書 全点ポイント30倍キャンペーン(~6/28)
  • ポイント 1,830pt (実際に付与されるポイントはご注文内容確認画面でご確認下さい)
  • 言語:ENG
  • ISBN:9781009686723
  • eISBN:9781009686716

ファイル: /

Description

From genome sequencing to large sky surveys, digital technologies produce massive datasets that promise unprecedented scientific insights. But data, for being good to use and reuse, need people – scientists, technicians, and administrators – as embodied, evaluative, social humans. In this book, anthropologist Götz Hoeppe draws on an ethnography of astronomical research to examine the media and practices that scientists and technicians use to instruct graduate students, make diagrams for data calibration and discovery, organize collaborative work, negotiate the ethics of open access, encode their knowledge in datasets – and undertake social inquiries along the way. This book offers a reflection on the sociality of data-rich research that will benefit attempts to integrate human and machine learning. It will be of interest for students and scholars in data science and science and technology studies, as well as in anthropology, sociology, history, and the philosophy of science. This book is also available Open Access on Cambridge Core.

Table of Contents

Preface; Acknowledgements; Introduction: making sense of data-centric socialities; 1. Medium: digital affordances; 2. Evaluations: the ethical life of data production; 3. Membership: learning to become a competent data user; 4. Diagrams: spaces for cultivating data and making discoveries; 5. World: mundane reason and the relief from trust in data makers; 6. Organizing: social, medial, and epistemic orders in data-centric collaboration; 7. Normativity: inhabiting statuses in 'Open Science'; 8. Encoding knowledge: how to make data speak for themselves; Outlook: scientific data, artificial intelligence, and people; Appendix: Transcription conventions; References; Index.

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