Computational Social Science for the Public Good: Towards a Taxonomy of Governance and Policy Challenges

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Abstract

Computational Social Science (CSS) has grown exponentially as the process of datafication and computation has increased. This expansion, however, is yet to translate into effective actions to strengthen public good in the form of policy insights and interventions. This chapter presents 20 limiting factors in how data is accessed and analysed in the field of CSS. The challenges are grouped into the following six categories based on their area of direct impact: Data Ecosystem, Data Governance, Research Design, Computational Structures and Processes, the Scientific Ecosystem, and Societal Impact. Through this chapter, we seek to construct a taxonomy of CSS governance and policy challenges. By first identifying the problems, we can then move to effectively address them through research, funding, and governance agendas that drive stronger outcomes.

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Verhulst, S. G. (2023). Computational Social Science for the Public Good: Towards a Taxonomy of Governance and Policy Challenges. In Handbook of Computational Social Science for Policy (pp. 19–40). Springer International Publishing. https://doi.org/10.1007/978-3-031-16624-2_2

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