Doing Computational Social Science: A Practical Introduction. Dieser Artikel ist nicht verfügbar.
Sprache: Englisch
Verlag: SAGE Publications Ltd, 2021
- Erstausgabe
- Softcover
- Neu

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Artikelbeschreibung des Verkäufers
2022. 1st Edition. Paperback. . . . . .
Bestandsnummer des Verkäufers V9781526468185
- Titel
- Doing Computational Social Science: A Practical Introduction
- Autor
- John McLevey
- Verlag
- SAGE Publications Ltd
- Erscheinungsjahr
- 2021
- Zustand
- New
- Einband
- Softcover
- Sprache
- Englisch
- ISBN-10
- 1526468182
- ISBN-13
- 9781526468185
- Auflage
- 1. Auflage
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Über die Autorin bzw. den Autor
John McLevey is an Associate Professor in the Department of Knowledge Integration at the University of Waterloo (ON, Canada). He is also appointed to the Departments of Sociology & Legal Studies and Geography and Environmental Management, is a Policy Fellow at the Balsillie School of International Affairs, and a Member of the Cybersecurity and Privacy Institute at the University of Waterloo. His work is funded by research grants from the Social Science and Humanities Research Council of Canada (SSHRC) and an Early Researcher Award from the Ontario Ministry of Research and Innovation.
His current research project focuses on disinformation, censorship, and political deliberation in the public sphere across a wide variety of national contexts and political regimes. He wrote Doing Computational Social Science (SAGE Publishing, 2021) from his experiences as a researcher and advisor, as well as teaching courses in computational social science, data science, and research methods to students from diverse disciplinary backgrounds at the undergraduate and graduate level.
His current research project focuses on disinformation, censorship, and political deliberation in the public sphere across a wide variety of national contexts and political regimes. He wrote Doing Computational Social Science (SAGE Publishing, 2021) from his experiences as a researcher and advisor, as well as teaching courses in computational social science, data science, and research methods to students from diverse disciplinary backgrounds at the undergraduate and graduate level.
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