Semantic Admissibility and the Safety Bias of Human Language

Recent work on ousiometrics proposes that the essential meaning of language is better represented by a power-danger-structure framework than by the traditional valence-arousal-dominance model. Large-scale analyses of word types and word tokens across diverse corpora further indicate a systematic linguistic bias toward safety. This paper incorporates that result into the Fractal Consistency Law (FCL) and its Principle of Minimum Inconsistency (PMI), while explicitly limiting the scope of the claim: the ousiometrics result does not validate the physical or cosmological components of the FCL. Instead, it provides a high-value conceptual and metatheoretical bridge for the FCL-PMI extension to symbolic, cognitive, social, and artificial-intelligence systems. The central thesis is that human language can be interpreted as a system of semantic admissibility: a symbolic technology that does not merely express affect or transmit propositional information, but helps make the social world habitable by reducing danger, disorder, interpretive ambiguity, and destabilizing power asymmetries. In FCL-PMI terms, language tends to reduce a semantic inconsistency charge, denoted by Csem, defined over danger, unstructuredness, power mismatch, memory, and contextual deviation. The paper introduces a formal model for semantic admissibility, derives a Gibbs-type distribution for safety-biased token usage, formulates dynamic and fractional-memory extensions, proposes falsifiable predictions, and outlines an empirical audit protocol based on time-series corpora, ousiometric coordinates, spectral analysis, detrended fluctuation analysis, changepoint detection, and cross-domain validation. The paper argues that the safety bias is best treated as gold-bridge evidence for the broader FCL-PMI thesis that persistent systems tend to form admissibility filters: physics filters ontological inconsistency, science filters empirical inconsistency, AI filters statistical-discursive inconsistency, and language filters semantic-social inconsistency. The resulting framework, Semantic Admissibility Theory (SAT-FCL), provides a rigorous bridge between complex systems, computational linguistics, risk perception, social stability, AI alignment, and the FCL-PMI architecture of reality as constrained persistence under admissibility.