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Computational social science: Obstacles and opportunitites
Date
2020
Author(s)
Lazer, David
Pentland, Alex
Watts, Duncan J.
Aral, Sinan
Athey, Susan
Contractor, Noshir
Freelon, Deen
Gonzalez-Bailon, Sandra
King, Gary
Margetts, Helen
Nelson, Alondra
Salganik, Matthew J.
Strohmaier, Markus
Vespignani, Alessandro
Wagner, Claudia
DOI
https://doi.org/10.1126/science.aaz8170
Abstract
Data Sharing, research ethics, and the incentives must improve. The field of computational social science (CSS) has exploded in prominence over the past decade, with thousands of papers published using observational data, experimental designs, and large-scale simulations that were once unfeasible or unavailable to researchers. These studies have greatly improved our understanding of important phenomena, ranging from social inequality to the spread of infectious diseases. The institutions supporting CSS in the academy have also grown substantially, as evidenced by the proliferation of conferences, workshops, and summer schools across the globe, across disciplines, and across sources of data. But the field has also fallen short in important ways. Many institutional structures around the field—including research ethics, pedagogy, and data infrastructure—are still nascent. We suggest opportunities to address these issues, especially in improving the alignment between the organization of the 20th-century university and the intellectual requirements of the field.
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Name
Lazer-etal_Computational-social-science_2020.pdf
Description
Computational Social Science: Obstacles and Opportunities. Science 369 (6507): 1060-1062
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190.15 KB
Format
Adobe PDF
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