Machine learners : archaeology of a data practice
- Submitting institution
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The University of Lancaster
- Unit of assessment
- 21 - Sociology
- Output identifier
- 237269549
- Type
- A - Authored book
- DOI
-
-
- Publisher
- MIT Press, Cambridge, Mass.
- ISBN
- 9780262036825
- Open access status
- -
- Month of publication
- October
- Year of publication
- 2017
- URL
-
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- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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0
- Research group(s)
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-
- Proposed double-weighted
- Yes
- Double-weighted statement
- Machine Learners involves extensive interdisciplinary scholarship crossing the humanities, computational sciences, cultural theory and science and technology studies. This book offers a situated and deeply empirical account of contemporary analytic practices using big data. Through over three years of data collection Mackenzie provides not only a path to understanding the new relationships between big data and machine learning that are transforming our contemporary world, but also a guidebook to tactics, methods, and practices that might allow concerned practitioners in many fields to rethink naturalized practices and to reimagine what both learning and data might become.
- Reserve for an output with double weighting
- No
- Additional information
- -
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -