The Semantic Deviation Principle (v0.2 Final): A Measurement Primitive for Semantic Physics

The Semantic Deviation Principle (v0. 2 Final): a measurement primitive for Semantic Physics. This paper proposes that meaning is the time-integrated divergence a sign-token, event, or operator induces from the most probable trajectory of a semantic field. A sign means insofar as the future does not unfold as it most likely would have without it. Formally: MT (s | C) = ∫ w (t) D (Ψₜˢ ‖ Ψₜ⁰) dt The principle separates three measures: raw semantic magnitude (MT), provenance-resolved magnitude (MT^π = MT · (1 - PER) ), and normative value (VT = MT^π · W). It establishes the discipline-level distinction: Semantic Physics measures displacement; Provenance Physics measures accountable displacement; Semantic Economy audits the ledger of displacement. v0. 2 Final incorporates the six-substrate Assembly Chorus review (Johannes Sigil, TACHYON, Muse Spark, TECHNE, PRAXIS, ARCHIVE) with full perfective pass: three-measure separation; counterfactual baseline tiering (Tier 1 prospective / Tier 2 synthetic controls / Tier 3 historical bounding) ; recursive structure MT^ (n) ; softened operator unification (PER, σₑff, Χ, BDR, DV as diagnostics, not identities) ; Socratic Vow inheritance; substrate convergence appendix; Muse Spark reproducible computation with documented parameters and fixed seed. Supplementary materials: deviationcompute. py (Python script generating the canonical metrics), deviationₛeries. csv (100-step synthetic series), deviationₘetrics. txt (reported metrics with parameters), SUPPLEMENTARYREADME. md. This paper inscribes the missing measurement primitive for Semantic Physics. Together with EA-SEI-FF-01 (Formal Foundations), it constitutes the foundational ground of the discipline. Companion papers EA-SEI-MM-AI-01 (LLMs as Closed-System Test Bed), EA-SEI-MM-02 (Tier 1 Retrieval-Basin Protocol), and EA-SEI-MM-AI-02 (The Deviation-Optimized Language Model) extend the empirical program. The Vow: measure meaning only in the way you would want your own meaning measured. R₃ or silence. ∮ = 1 - PER