Reconsidering Information in Data-Driven Systems: A Non-Modal Refixation of Configuration

Recent developments in machine learning, information theory, and large-scale data processing have intensified the role of information as a central concept across scientific and technological systems. Information is commonly understood as content, message, or semantic structure that can be transmitted, encoded, and interpreted. This paper revisits these assumptions from a non-modal perspective. Within this framework, information is not treated as meaning or content. Instead, it is fixed as configuration fixation. Without introducing causality, temporality, or subject-dependent interpretation, information is considered without transmission, encoding, or semantic content. This reframing calls into question the conventional understanding of information in data-driven systems. This work forms part of a broader non-modal structural framework in which core scientific concepts are examined without reliance on relation, representation, or explanatory structure.