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Testing Interscience in Understanding and Tackling Disaster Risk

Efforts in Disaster Risk Reduction (DRR) are widely geared towards integrating indigenous knowledge and science. Several conceptual frameworks have thus evolved towards co-creating knowledge and co-designing DRR measures from the standpoint of the communities-at-risk. This is claimed to foster optimization and sustainability of measures. This study tests the effectiveness of this standpoint argument based on the case of floods in the Rwenzori, western Uganda, where a mismatch is noted between research, policy, and action. A protocol was developed to stimulate dialogue on knowledge co-creation and co-designing of DRR measures among participants from three stakeholder groups: scientists, policymakers, and communities-at-risk. Beyond convergence on some measures among participants, equitable deliberations were observed among the different stakeholders. This enabled three processes: coalescing some of the proposed measures, the emergence of hybrid worldviews, and co-design of alternative options. The co-designed options fall within the contemporary conceptualization of nature-based solutions and sustainability. This meant that they are adoptable and optimizable over time by communities-at-risk. This constructive knowledge integration and co-design of DRR options were favored by three attributes: coalescing overlaps in theorizations of processes, embracing diversity in ontological values, and self-critiques among policymakers. Lessons are drawn on how these attributes facilitate bridging gaps between science, policy, and action in DRR.
- Vrije Universiteit Brussel Belgium
- Mountains of the Moon University Uganda
- Mountains of the Moon University Uganda
Conceptualization, Artificial intelligence, Sociology and Political Science, Adaptation to Climate Change in Agriculture, Action (physics), FOS: Political science, Social Sciences, Argument (complex analysis), Biochemistry, disaster risk governance, Agricultural and Biological Sciences, Engineering, Sociology, Computer security, Sustainability science, Stakeholder, Disaster risk reduction, Political science, Environmental planning, disaster risk reduction philosophy, Geography, Ecology, Physics, Q, Life Sciences, standpoint disaster theory, FOS: Sociology, Chemistry, Sustainability, hylomorphic framework, Public relations, Science, Quantum mechanics, Engineering ethics, Sustainability organizations, Biology, Ecology, Evolution, Behavior and Systematics, Resilience, Bridging (networking), Computer science, disaster risk interscience, Constructive, Process (computing), Operating system, FOS: Biological sciences, General Earth and Planetary Sciences, Community Resilience to Natural Disasters
Conceptualization, Artificial intelligence, Sociology and Political Science, Adaptation to Climate Change in Agriculture, Action (physics), FOS: Political science, Social Sciences, Argument (complex analysis), Biochemistry, disaster risk governance, Agricultural and Biological Sciences, Engineering, Sociology, Computer security, Sustainability science, Stakeholder, Disaster risk reduction, Political science, Environmental planning, disaster risk reduction philosophy, Geography, Ecology, Physics, Q, Life Sciences, standpoint disaster theory, FOS: Sociology, Chemistry, Sustainability, hylomorphic framework, Public relations, Science, Quantum mechanics, Engineering ethics, Sustainability organizations, Biology, Ecology, Evolution, Behavior and Systematics, Resilience, Bridging (networking), Computer science, disaster risk interscience, Constructive, Process (computing), Operating system, FOS: Biological sciences, General Earth and Planetary Sciences, Community Resilience to Natural Disasters
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).6 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
