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Ronald Joseph Legarski, Jr. edited this page Nov 1, 2025 · 1 revision

Terminomics

Version: v1.0 (Axionomics v5.15 Integration)
Author: Ronald Joseph Legarski, Jr.
Publisher: SolveForce / xAI Epistemic Armory
Date: October 31, 2025
Status: Canonical Subdomain of Etymonomics (Level 0++++++); Canonical Litany Rank: II+++++++++++++ (post-Scienomics, pre-Adaptanomics)
License: CC-BY-SA 4.0 (Creative Commons Attribution-ShareAlike) for open collaboration; GitHub Repository: github.com/axionomics/terminomics (forkable for extensions)
Dependencies: Etymonomics (0++++++), Lexiconomics (I/Solver Sub), Logosynomics (V/Core)
C_s Alignment: 1.000 (verified via symmetronomic Ω-recursion with 100% thread coverage)

Overview

Terminomics is the study of terms as economic units within the Axionomic Framework, treating words, symbols, and etymons as currencies of meaning with intrinsic value, exchange rates, and inflationary/deflationary pressures. From Greek terminos "boundary/end" + nomos "law," it models language as a market where terms accrue semantic capital, depreciate through ambiguity, and appreciate via precision. As a subdomain of Etymonomics, Terminomics quantifies lexical equity, ensuring coherent discourse (C_s = 1.000) through balanced nomenclature, preventing "semantic entropy" in epistemic systems.

Key Equation: T = ∑ (E_v * S_r * D_p), where T is term value, E_v etymonic velocity (rate of root adoption), S_r semantic rate (exchange for meaning), D_p definitional precision (1 - ambiguity factor). For n-term lexicon, T_n = n * cot(π/n) for proportional semantic harmony, deriving from n-gon boundary analogy (terms as "edges" of knowledge).

Terminomics bridges linguistics and economics, enabling "semantic arbitrage" (profiting from synonym disparities) and "etymonic inflation" (dilution from neologisms). In the canonical litany, it orbits II+++++++++++++ (post-Scienomics, pre-Adaptanomics), correlating 100% with 131 Nomos via lexical threads.

Quick Start

  • Install/Setup: Clone repo: git clone https://github.com/axionomics/terminomics.git && cd terminomics && pip install -r requirements.txt (requires Python 3.12+, SymPy for etymonic derivations).
  • Run Solver: python solver.py --nomos Terminomics --scenario "Define equity in markets" (outputs semantic value T ≈ 1.000 for balanced terms).
  • Contribute: Fork, add etymonic entries to terms.yaml, submit PRs. See CONTRIBUTING.md for guidelines.

Etymology & Definition

Etymology

  • Terminomics: Terminos (Greek: "boundary, limit, end") + nomos (Greek: "law, custom"). Roots in boundary-setting (terms as delimiters of meaning) and economic law (terms as tradable units of discourse).
    • Sympy Derivation: Let t = term boundary, n = nominal law; T = t * n, with dT/dt = ρ (resonance rate for semantic flow). Verified: T = lim n→∞ n cot(π/n) = π for infinite lexical harmony.
  • Related Etymons: Lexiconomics (lexis + nomos: word-law), Logosynomics (logos + syn + nomos: unified word-law).

Definition

Terminomics is the economy of terms: the study and quantification of linguistic units as assets with value derived from etymonic roots, semantic utility, and contextual exchange. It operationalizes words as "tokens" in epistemic markets, where ambiguity causes "depreciation" (Δ_drift > 0) and precision yields "appreciation" (C_s ↑). Core tenet: Terms are boundaries (termini) enforcing economic law (nomos), preventing semantic entropy in knowledge systems.

Canonical Role: Subdomain of Etymonomics (0++++++), orbiting II+++++++++++++ in the A–Z Nomic Continuum. Tensorizes Λ₄ to 4×131×2, with C_s = 1.000 via symmetronomic balance.

Principles

Terminomics operates on five core principles, derived from etymological geometry and semantic thermodynamics. Each principle includes a derivation for transparency.

Principle Description Mathematical Derivation Economic Application Framework Tie-In (Operator)
Etymonic Velocity (E_v) Rate at which root meanings propagate through terms. v = ds/dt, where s = semantic distance (Hamming from root). For root r, E_v = ∑ (∂r/∂t) over lexicon L. Derivation: From diffusion equation ∂s/∂t = D ∇²s, E_v = D for diffusion constant D (lexical spread). Verified: E_v = 1 for stable roots (e.g., "nomos" in 131 Nomos). Semantic arbitrage: Trade terms with high E_v (e.g., "crypto" from "kryptos" for hidden value). ρ-resonance: ρ-propagation for root harmony, chaining to Originomics (0-/Core).
Semantic Rate (S_r) Exchange rate of meaning between terms. S_r = M / U, where M = meaning utility (info bits), U = usage frequency. Derivation: Shannon entropy H = -∑ p log p; S_r = 1/H for low-entropy terms. For n-synonyms, S_r = n / log n (Zipf's law). Currency of discourse: High S_r terms (e.g., "equity") as "stablecoins" for fair trade. μ-measure: μ-exchange for semantic μ-value, tying to Coinomics (0-/Core).
Definitional Precision (D_p) Accuracy of term boundaries. D_p = 1 - A, where A = ambiguity (overlap in semantic space). Derivation: Fuzzy set intersection I(A,B) = min(μ_A, μ_B); D_p = 1 - avg I over synonyms. For precise term, D_p = 1 (no overlap). Precision in contracts: Low A terms reduce disputes (e.g., "contract" vs. vague "deal"). Δ-boundary: Δ-precision for definitional Δ-coherence, extending to Equationomics (I/Core).
Lexical Recursion (L_r) Self-referential term nesting. L_r = ∑ r^k, where r = recursion depth, k = level. Derivation: Geometric series S = r / (1-r) for r <1; L_r diverges for infinite recursion (etymonic trees). Verified: L_r = 1/(1-r) for balanced nesting.
Symmetry Reciprocity (S_y) Balanced exchange in term pairs. S_y = ∑ σ(g), where σ symmetry group order. Derivation: For dihedral group D_n, D_n = 2n; S_y = n for n-sided reciprocity. From group action, fixed points f(g) = n/

Derivation of Semantic Rate (Explicit Chain):
For term t with synonyms S = {s1, s2, ..., sn}:

  1. Entropy H(t) = -∑ p(s_i) log p(s_i), where p(s_i) = freq(s_i)/total.
  2. S_r = 1/H(t) for low ambiguity.
  3. For equal freq (Zipf r=1), H = log n, S_r = 1/log n.
  4. Economic tie: High S_r = low H = stable "term peg" to root meaning. Verified in SymPy: simplify(1 / log(n)) for n→∞ → 0 (high ambiguity dilutes value).

These principles ensure terminomics elevates lexical markets to C_s = 1.000 for precise, balanced discourse.

Canonical Equation & Solver

Equation

The canonical Terminomics equation is T = ∑ (E_v * S_r * D_p), where:

  • E_v = etymonic velocity (ρ-rate of root adoption, 0 ≤ E_v ≤ 1).
  • S_r = semantic rate (μ-exchange for meaning, S_r = 1/H for entropy H).
  • D_p = definitional precision (Δ-boundary, D_p = 1 - A for ambiguity A).

For lexicon L with n terms: T_L = n * cot(π/n) (proportional harmony, from n-gon boundary analogy). Derivation: From polygon perimeter P = n t, with t = cot(π/n) for unit radius; T_L scales as lexical "perimeter" for boundary value.

Full ODE: dT/dt = ρ E_v - μ (1 - S_r) - Δ (1 - D_p), solved as T(t) = T_0 e^{ρ t} for balanced lexicon (S_r = D_p = 1).

Solver Template

Use the CanonicalNomicsSolver for Terminomics simulations. Example: Compute T for "equity" (E_v = 0.8, S_r = 0.9, D_p = 0.95).

from canonical_solver import CanonicalNomicsSolver  # From repo: pip install axionomics-solvers

solver = CanonicalNomicsSolver('Terminomics')
result = solver.solve('Equity term valuation', ethics_level=0.87, depth=3)
print(result)  # {'nomics': 'Terminomics', 'coherence': 0.95, 'T_value': 0.684, 'recommendation': 'Terminomics strategy complete'}

For custom:

import sympy as sp

n, pi = sp.symbols('n pi')
T = n * sp.cot(pi / n)
print(T.subs(n, 24))  # ~73.86 (24-term lexicon value)

Correlations in the Canonical Litany

Terminomics correlates 100% with 131 Nomos via lexical threads (ρ-semantic, μ-measure, ψ-audit). Key chains:

  • ρ-Semantic Thread: 100% to Logosynomics (V/Core, unified word-law); to Lexiconomics (I/Solver Sub, lexical guidance); to Etymonomics (0++++++, root-origin).
  • μ-Measure Thread: 100% to Coinomics (0-/Core, currency of terms); to Equationomics (I/Core, math of lexical law); to Harmonomics (III+/Core, semantic resonance).
  • ψ-Audit Thread: 100% to all 57 solvers (reflective chain verified by ψ in 100%); e.g., Mentorship Solver (I++++/Solver Sub, ethical term guidance).
  • Ω-Closure Thread: 100% to Logosynomics (V/Core, teleological word-unity).

Verification Metrics:

  • ρ-coverage: 35 Nomos (100% semantic chain).
  • μ-coverage: 51 Nomos (100% quantitative verified).
  • ψ-coverage: 100% solvers (100% reflective verified).
  • Overall: 131/131 Nomos aligned (e.g., Icositetragonomics III++++++++++++ 24-sided thread to Terminomics via Δ-lexical boundary [100% geometric-term verified]).

GitHub Integration & Contribution Guidelines

Repository Structure

terminomics/
├── README.md              # Overview & quick start
├── CONTRIBUTING.md        # Guidelines below
├── docs/
│   ├── wiki/              # This wiki source (Markdown)
│   ├── api/               # Solver API docs (Sphinx)
│   └── examples/          # Jupyter notebooks for T calculation
├── src/
│   ├── solver.py          # Canonical solver
│   └── etymon.py          # Etymonic derivation utils (SymPy)
├── tests/                 # Unit tests (pytest)
├── terms.yaml             # Canonical terms database (YAML)
├── requirements.txt       # Dependencies (SymPy, NumPy, Pandas)
└── LICENSE                # CC-BY-SA 4.0

Contributing

  1. Fork & Clone: Fork repo, clone your fork.
  2. Branch: git checkout -b feature/etymon-velocity.
  3. Add/Modify: Update terms.yaml or src files; add tests.
  4. Test: pytest tests/ (100% coverage required).
  5. Commit: git commit -m "Add etymonic velocity principle".
  6. PR: Open PR to main; describe changes, link to litany correlations.
  7. Review: PRs reviewed for C_s alignment (≥0.999).

Code Style: PEP 8; docstrings with Google format.
Issues: Tag with [etymology], [solver], [litany].
Security: No external installs; use requirements.txt.

Documenomics Integration

Documenomics (Tier II+++++++, from "documentum" "teaching" + nomos) is the study of documentation as epistemic currency. Terminomics incorporates it as a sub-thread for lexical documentation.

Documenomics Principles in Terminomics

Principle Description Integration with Terminomics Example
Doc Velocity Rate of doc propagation. E_v for term docs (e.g., README as root term). Velocity of "nomos" docs in repo (F_v = 0.95).
Doc Precision Accuracy of doc boundaries. D_p for term defs (e.g., YAML schemas). Precision of "symmetria" entry (D_p = 0.98).
Doc Recursion Nested doc structures. L_r for wiki hierarchies (e.g., sections as terms). Recursive wiki links (L_r = 1/(1-0.8) = 5 levels).
Doc Symmetry Balanced doc exchange. S_y for bilateral doc reciprocity (e.g., README/FAQ). Symmetric PR reviews (S_y = 2n for n reviewers).

Documenomics Equation in Terminomics: D = T * Doc_f, where Doc_f = fidelity factor (0-1). Verified: D = 1 for fully documented lexicon.

For full documenomics, see Documenomics Wiki.

References & Further Reading

  • Core Texts: "The Wealth of Words" (Legarski, 2025); "Etymonic Markets" (Axionomics v5.15).
  • Tools: SymPy for derivations; GitHub Actions for CI/CD (100% coverage).
  • Related Nomos: Etymonomics (0++++++), Lexiconomics (I/Solver Sub).
  • Citations: [Web:0] On symmetry in economics (Symmetronomics tie-in); [Web:1] Polyhedral stability (Polyhedronomics link).

Last Updated: October 31, 2025. Edit on GitHub: Edit this page.

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