Beyond Containment: Establishing the Critical Parameters of Mythotechnic Fiction

Abstract

This paper establishes the critical, methodological, and quantitative framework for distinguishing Mythotechnic Fiction, Meta-Mythic Delphic Fiction, and Ontological Systems Fiction from adjacent literary genres and subgenres. It develops the multidimensional genre-space model, defines the structural factors used to classify works, and formalizes concepts including Lexical Association Misattribution, Scalar Comparative Reduction, Architectural Misclassification, Mythotechnic Density, and Architectonic Authorship. The study addresses the problem of literary works being misclassified through isolated words, motifs, or partial similarities rather than through analysis of their governing narrative architecture. The paper then operationalizes the proposed dimensions into a work-level coding instrument and applies them to a pilot corpus consisting of Martinez texts and strong comparative works drawn from science fiction, theological fiction, metafiction, cyberpunk, prophetic fiction, and related traditions. Statistical analysis includes descriptive statistics, Welch comparisons, effect sizes, principal component analysis, multidimensional centroid distance, and graphical analysis of mean dimensional reach and within-work dispersion. The results indicate that the strongest distinctions do not arise from mythology, cosmology, or complexity alone, but from the structural integration of institutional power, machine authority, ontological horror, and generative frameworks. The paper proposes that architectonic integration may be measurable as sustained activation across multiple structural dimensions rather than as thematic accumulation alone. The study is presented as an exploratory, single-coder Phase II validation. It does not rank literary quality or claim final population-level proof. Instead, it establishes a reproducible research design and identifies independent corpus coding, inter-rater reliability, larger samples, and multivariate validation as the next stages of testing.