Science-like knowledge as epistemic infrastructure for climate disaster governance

Purpose We introduce science-like knowledge to address gaps in predictive climate disaster systems that fail to capture hyper-local impacts. Conceived as a complementary epistemic infrastructure to formal science, it extends beyond anecdotal social knowledge to include structured, real-time insights generated by communities and circulated through social media. Design/methodology/approach Drawing on conceptual analysis of citizen observations, digital reporting, and grassroots monitoring, we synthesise developments in disaster research and vernacular sensing to demonstrate the epistemic need for alternative knowledge streams. Findings Integrating science-like knowledge improves early-warning precision, situational awareness, and adaptive response. Communities function as epistemic contributors, offering granular data that can verify and contextualise model outputs in data-saturated environments. Research limitations/implications Embedding science-like knowledge within disaster risk governance (DRG) promotes cognitive justice, strengthens local participation, and better aligns preparedness with uneven climate volatility. We propose a five-step translational framework for operational integration. Originality/value Science-like knowledge is conceptualised as a distinct and structurally coherent category of disaster-relevant information that neither replicates scientific method nor collapses into anecdote. Community-generated digital data and vernacular sensing are reframed as critical epistemic infrastructures, particularly in Global South contexts.