Semantics (Truth Fidelity)

Definition

Semantics (Truth Fidelity) is the foundational science concerned with the accuracy of meaning: the degree to which information corresponds to reality, maintains internal clarity, and is transmitted without distortion. It underpins the “T” (Truth Integrity) variable in Meaning System Science and establishes the standards by which a system perceives, represents, and communicates truth.

Truth Fidelity integrates classical semantics, modern linguistic theory, logic, philosophy of language, and information science into a unified study of how meaning stays anchored to reality.

Intellectual Lineage

Semantics as a discipline has deep scientific and philosophical roots. Meaning System Science draws from:

  • Gottlob Frege – sense and reference

  • Bertrand Russell – logical atomism, correspondence

  • Alfred Tarski – semantic truth theory (“T-schema”)

  • Ludwig Wittgenstein – language games, meaning as use

  • Noam Chomsky – generative grammar, linguistic structure

  • Donald Davidson – truth-conditional semantics

  • Saul Kripke – naming and necessity, rigid designation

  • Hilary Putnam – semantic externalism

These theorists established the foundation for understanding how meaning reflects reality.

Meaning System Science extends this lineage by treating truth fidelity not only as a linguistic function but as a structural force in systems.

Truth Fidelity in Meaning System Science

Truth Fidelity is one of the five foundational forces that shape meaning behavior (T, P, C, D, A).

In MSS, Truth Fidelity measures:

  • how accurately a system perceives reality

  • how reliably information is verified

  • how clearly truth moves through communication channels

  • how consistently truth is reflected in decisions

  • how quickly distortions accumulate under pressure

A system’s truth fidelity determines whether meaning remains coherent or drifts.

Truth Fidelity vs. Truth Integrity

The discipline distinguishes between:

Truth Fidelity

The raw accuracy of information and perception.

Truth Integrity (T)

The structural expression of truth fidelity within the meaning system: how truth moves, persists, and is preserved through communication.

Truth Fidelity belongs to the foundational science (Semantics).
Truth Integrity belongs to the structural variables of Moral Physics and MSS.

Role in the Legitimacy Equation

Truth Fidelity provides the conceptual basis for:

T = Truth Integrity
in
L = (T × P × C) ÷ D

Without high-fidelity information, a system cannot reach proportional legitimacy, regardless of power or coherence.

Truth Fidelity errors amplify drift and degrade signals.

Relationship to Other Foundational Sciences

Semeiology (Signal Behavior)

Truth Fidelity determines whether signals accurately carry meaning.

Systems Theory (Structural Coherence)

Truth Fidelity influences whether structure can reliably conduct correct information.

Thermodynamics of Meaning (Drift & Entropy)

High-fidelity truth slows the rate at which drift accumulates.

Affective Science (Regulation)

Truth Fidelity reduces emotional distortion and reactionary behavior.

Semantics in Organizational Life

Truth Fidelity determines:

  • whether leaders understand reality accurately

  • whether communication matches conditions on the ground

  • whether decisions reflect real constraints

  • whether strategy aligns with truth or fantasy

  • whether signals reinforce or undermine reality

Organizations with poor truth fidelity experience:

  • fragmentation

  • drift

  • incoherence

  • political distortion

  • burnout

  • meaning collapse

Organizations with strong truth fidelity experience:

  • clarity

  • resilience

  • coherence

  • aligned decision-making

  • low drift

  • high legitimacy

Semantics is not abstract, it is the starting point of structural reliability.

Applications in Meaning System Science & Transformation Management

Semantics (Truth Fidelity) is used to:

  • calibrate system truth-reading

  • analyze information flow

  • detect truth distortion mechanisms

  • ensure signals reflect reality

  • diagnose truth-to-power breakdowns

  • measure the accuracy of mental models

  • evaluate epistemic resilience

  • prevent truth drift in AI-accelerated environments