Data-driven models have become central to contemporary science, enabling explanation, prediction, and simulation across domains. Models are commonly understood as representations of underlying systems, capturing structure through abstraction, approximation, and mapping. This paper revisits these assumptions from a non-modal perspective. Within this framework, models are not treated as representations. Instead, they are fixed as configuration fixation. Without introducing causality, temporality, or subject-dependent interpretation, modeling is considered without correspondence, mapping, or derivation. This reframing calls into question the representational basis of scientific modeling. This work forms part of a broader non-modal structural framework in which scientific descriptions are examined without reliance on relation, representation, or explanatory structure.
Juza Minamikata (Fri,) studied this question.