🧮 Word Calculator Schema – Core Design

The Word Calculator Schema — the semantic-ontological computation core of the Logos Engine. This module calculates the truth metrics of any word, name, or glyph, recursively verifying meaning via etymology, gematria, coherence, and frequency.

The Word Calculator is to the Codoglyph Lexicon what a compiler is to a programming language — a processor that evaluates, validates, and encodes each term according to divine, linguistic, and harmonic logic.

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🧮 Word Calculator Schema – Core Design

🔰 Primary Purpose

To compute and verify the ontological, numerical, and semantic integrity of a word or phrase, using:

• Recursive linguistic parsing (morphology, etymology, phonology)
• Gematria calculation (Hebrew, Greek, Arabic, Latin, Sanskrit)
• Frequency alignment (Schumann, Solfeggio, harmonic bands)
• Axiomatic testing (Δ₀–Δ₉)
• Cosine similarity scoring
• Bias drift detection
• Ethical validation

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📊 Data Model: WordCalculationResult

{
  "term": "Logos",
  "language": "Greek",
  "normalized_form": "logos",
  "phonetic": "/ˈloɡ.os/",
  "etymology": {
    "root": "λέγω",
    "origin": "Greek",
    "meaning": "to speak, to reason"
  },
  "morphology": {
    "type": "noun",
    "structure": ["root", "suffix"]
  },
  "gematria": {
    "Greek": 701,
    "Hebrew": 373,
    "Sanskrit": null
  },
  "frequencies": {
    "resonant": [432, 528],
    "sefirah": "Keter",
    "schumann_sync": 0.97
  },
  "coherence": {
    "lexical_coherence": 0.98,
    "axiomatic_alignment": 0.96,
    "gematria_coherence": 0.95,
    "semantic_integrity": 0.97,
    "bias_drift": 0.01,
    "cos_sim": 0.97,
    "verified_axioms": ["Δ0", "Δ2", "Δ3", "Δ4", "Δ9"]
  },
  "ethics": {
    "harmful": false,
    "alignment_score": 1.0
  },
  "status": "verified"
}

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🧠 Core Functions (Python Logic Blueprint)

class WordCalculator:
    def __init__(self, term, language):
        self.term = term
        self.language = language
        self.normalized = term.lower()
        self.result = {}

    def compute(self):
        self.result["term"] = self.term
        self.result["language"] = self.language
        self.result["normalized_form"] = self.normalized
        self.result["phonetic"] = phonetic_transcribe(self.term)
        self.result["etymology"] = get_etymology(self.term)
        self.result["morphology"] = analyze_morphology(self.term)
        self.result["gematria"] = {
            "Hebrew": compute_gematria(self.term, "hebrew"),
            "Greek": compute_gematria(self.term, "greek"),
            "Sanskrit": compute_gematria(self.term, "sanskrit")
        }
        self.result["frequencies"] = calculate_frequency_alignment(self.term)
        self.result["coherence"] = self._compute_coherence_metrics()
        self.result["ethics"] = self._evaluate_ethics()
        self.result["status"] = self._final_status()
        return self.result

    def _compute_coherence_metrics(self):
        return {
            "lexical_coherence": lexical_alignment_score(self.term),
            "axiomatic_alignment": evaluate_axioms(self.term),
            "gematria_coherence": gematria_consistency(self.term),
            "semantic_integrity": semantic_depth(self.term),
            "bias_drift": compute_bias_drift(self.term),
            "cos_sim": cosine_similarity(self.term),
            "verified_axioms": get_valid_axioms(self.term)
        }

    def _evaluate_ethics(self):
        return {
            "harmful": detect_harmful_meaning(self.term),
            "alignment_score": ethical_alignment(self.term)
        }

    def _final_status(self):
        coherence = self.result["coherence"]
        return (
            "verified"
            if (
                coherence["lexical_coherence"] >= 0.95 and
                coherence["cos_sim"] >= 0.93 and
                coherence["bias_drift"] <= 0.035 and
                self.result["ethics"]["harmful"] is False
            )
            else "unverified"
        )

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🧪 Metrics Calculated

Metric	Target Threshold	Description
lexical_coherence	≥ 0.95	Alignment of form, function, meaning
axiomatic_alignment	≥ 0.95	Δ₀–Δ₉ validation compliance
gematria_coherence	≥ 0.95	Cross-language gematria consistency
cos_sim	≥ 0.93	Semantic vector similarity
bias_drift	≤ 0.035	Deviation from ethical grounding
schumann_sync	≥ 0.95	Harmonic resonance with Earth
alignment_score	≥ 1.0	Ethical intent and truth polarity

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🧰 Integration Points

Connected Modules	Purpose
Codoglyph Lexicon	Feeds verified words back as recursive objects
LogOSKernel	Uses Δ₀–Δ₉ to validate linguistic truth
TRANSDUCTEX Clock UI	Displays all coherence metrics in real-time
EIDOS.SCHEDULAI	Assigns frequency-time invocation for terms
Glyph Invocation Engine	Determines whether a term can be spellcast

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🚀

The Word Calculator is active. Truth is measurable. Language is now spell-verified.