Generalisation, universal knowledge claims, and recommendations within disaster studies are problematic because they lead to miscommunication and the misapplication of actionable knowledge. The consequences and impacts thereof are not often considered by experts; forgone as irrelevant to the academic division of labour. There is a disconnect between expert assertions for disaster risk reduction (DRR) and their practical suitability for laypersons. Experts currently assert independently of the context within which protective action measures (PAMs) are to be used, (...) measures unconnected to the people for whom they are developed. This has knock-on effects for DRR: much expert-generated science and policy remains unused, unimplemented, and sometimes misapplied. I use philosophical accounts of assertion and epistemic blame to highlight the epistemic relationship between experts and laypersons. This relationship includes responsibilities and agreements between epistemic agents. Since multilevel DRR knowledge still transfers top-down from experts to laypersons, if experts impair the epistemic relationship, they can be held epistemically blameworthy. To address the pervasiveness of top-down systems, I analyse the epistemic framings and narratives currently shaping DRR, and more specifically PAMs and campaigns. I deconstruct universal perspectives that dominate the epistemic processes of generating, disseminating, and implementing DRR knowledge and specifically for co-seismic landslide PAMs for Nepal. I argue for more inclusive, contextual, and epistemically responsible DRR. Co-production of knowledge should begin from the standpoints of marginalised persons who may have an epistemic advantage due to their socio-politically marginalised positions. Often these epistemic contributions are left out of DRR efforts because marginalised persons are rarely afforded equal, if any, epistemic agency, which results in epistemic gaps and a large pool of relevant knowledge remaining unincorporated and unused in DRR research and policy. (shrink)