Autor(es):
Holford, Dawn ; Fasce, Angelo ; Tapper, Katy ; Demko, Miso ; Lewandowsky, Stephan ; Hahn, Ulrike ; Abels, Christoph M. ; Al-Rawi, Ahmed ; Alladin, Sameer ; Sonia Boender, T. ; Bruns, Hendrik ; Fischer, Helen ; Gilde, Christian ; Hanel, Paul H. P. ; Herzog, Stefan M. ; Kause, Astrid ; Lehmann, Sune ; Nurse, Matthew S. ; Orr, Caroline ; Pescetelli, Niccolò ; Petrescu, Maria ; Sah, Sunita ; Schmid, Philipp ; Sirota, Miroslav ; Wulf, Marlene
Data: 2023
Identificador Persistente: https://hdl.handle.net/10316/112184
Origem: Estudo Geral - Universidade de Coimbra
Assunto(s): science communication; collective intelligence; epistemic diversity; knowledge aggregation; participatory input; knowledge updating
Descrição
Effective science communication is challenging when scientific messages are informed by a continually updating evidence base and must often compete against misinformation. We argue that we need a new program of science communication as collective intelligence-a collaborative approach, supported by technology. This would have four key advantages over the typical model where scientists communicate as individuals: scientific messages would be informed by (a) a wider base of aggregated knowledge, (b) contributions from a diverse scientific community, (c) participatory input from stakeholders, and (d) better responsiveness to ongoing changes in the state of knowledge.
Holford, Fasce, Lewandowsky and Schmid were supported by the European Commission Research and Innovation Horizon 2020 Grant 964728 (JITSUVAX). Lewandowksy and Abels were supported by the European Research Council (ERC Advanced Grant 101020961 PRODEMINFO). Lewandowsky was supported by the Humboldt Foundation through a research award. Hahn was supported by the Arts and Humanities Research Council (AHRC), UKRI. Herzog was supported by Deutsche Forschungsgemeinschaft (DFG) Grant 458366841 (POLTOOLS). Lehmann was supported by the Villum Foundation (34288). Nurse was supported by the Australian Government Research Training Program (RTP) scholarship. Sirota was supported by the European Commission Research and Innovation Horizon 2020 Grant 101016967 (YUFERING).
