Unleashing collective intelligence for public decision-making: the Data for Policy community

Since its establishment in 2014, Data for Policy (https://dataforpolicy.org) has emerged as a prominent global community promoting interdisciplinary research and cross-sector collaborations in the realm of data-driven innovation for governance and policymaking. This report presents an overview of th...

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Vydáno v:Data & Policy Ročník 6
Hlavní autoři: Engin, Zeynep, Gardner, Emily, Hyde, Andrew, Verhulst, Stefaan, Crowcroft, Jon
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cambridge Cambridge University Press 01.01.2024
Témata:
Artificial intelligence
collective intelligence
Committees
Community
Community research
COVID-19
Data
data for policy
Decision making
digital transformation
Frame analysis
Governance
government
Innovations
Intelligence
Interdisciplinary aspects
Interdisciplinary research
Interdisciplinary studies
Mapping
Policy analysis
Policy making
Policy research
Sustainable development
ISSN:2632-3249, 2632-3249
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Shrnutí:Since its establishment in 2014, Data for Policy (https://dataforpolicy.org) has emerged as a prominent global community promoting interdisciplinary research and cross-sector collaborations in the realm of data-driven innovation for governance and policymaking. This report presents an overview of the community’s evolution from 2014 to 2023 and introduces its six-area framework, which provides a comprehensive mapping of the data for policy research landscape. The framework is based on extensive consultations with key stakeholders involved in the international committees of the annual Data for Policy conference series and the open-access journal Data & Policy (https://www.cambridge.org/core/journals/data-and-policy), published by Cambridge University Press. By presenting this inclusive framework, along with the guiding principles and future outlook for the community, this report serves as a vital foundation for continued research and innovation in the field of data for policy.
Bibliografie:ObjectType-Article-1
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ISSN:2632-3249
2632-3249
DOI:10.1017/dap.2024.2