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

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Main Author: Minamikata, Juza
Format: Recurso digital
Language:English
Published: Zenodo 2026
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author Minamikata, Juza
author_facet Minamikata, Juza
contents <p>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.</p> <p> </p> <p>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.</p> <p> </p> <p>Within this framework, information is not treated as meaning or content. Instead, it is fixed as configuration fixation.</p> <p> </p> <p>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.</p> <p> </p> <p>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.</p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_19947776
institution Zenodo
language eng
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle Reconsidering Information in Data-Driven Systems: A Non-Modal Refixation of Configuration
Minamikata, Juza
non-modal structural system
configuration fixation
information theory
data-driven systems
non-semantic information
machine learning theory
<p>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.</p> <p> </p> <p>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.</p> <p> </p> <p>Within this framework, information is not treated as meaning or content. Instead, it is fixed as configuration fixation.</p> <p> </p> <p>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.</p> <p> </p> <p>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.</p>
title Reconsidering Information in Data-Driven Systems: A Non-Modal Refixation of Configuration
topic non-modal structural system
configuration fixation
information theory
data-driven systems
non-semantic information
machine learning theory
url https://doi.org/10.5281/zenodo.19947776