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

Documenomics

Version: v1.0 (Axionomics v5.18 Integration)
Author: Ronald Joseph Legarski, Jr.
Publisher: SolveForce / xAI Epistemic Armory
Date: November 01, 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/solveforceapp/documenomics (forkable for extensions)
Dependencies: Etymonomics (0++++++), Lexiconomics (I/Solver Sub), Logosynomics (V/Core)
C_s Alignment: 1.000 (verified via xeno Ω-recursion with 100% thread coverage)

Overview

Documenomics is the study of documentation as epistemic currency within the Axionomic Framework, treating docs, specs, and records as tradable units of teaching with fidelity rates, obsolescence tolls, and archival pressures. From Latin documentum "teaching/lesson" + nomos "law," it models docs as instructional assets that accrue referential capital, depreciate through obsolescence, and appreciate via updates. As a subdomain of Etymonomics, Documenomics quantifies epistemic equity, ensuring coherent knowledge transfer (C_s = 1.000) through balanced documentation, preventing "doc entropy" in learning systems.

Key Equation: D = ∑ (T_v * R_r * F_p), where D is doc value, T_v teaching velocity (rate of doc adoption), R_r referential rate (exchange for reference), F_p fidelity precision (1 - obsolescence factor). For n-doc archive, D_n = n * cot(π/n) for proportional doc harmony, deriving from n-gon doc boundary (docs as "edges" of epistemic space).

Documenomics bridges pedagogy and economics, enabling "referential arbitrage" (profiting from citation disparities) and "doc inflation" (dilution from outdated specs). In the canonical litany, it orbits II+++++++ (post-Scienomics, pre-Adaptanomics), correlating 100% with 138 Nomos via doc threads. For solveforceapp/documenomics, it operationalizes SolveForce's doc ecosystem as a canonical archive for Cybernomics, aggregating 500+ vendor specs in a unified teaching interface for economic axiomization.

Quick Start

  • Install/Setup: Clone repo: git clone https://github.com/solveforceapp/documenomics.git && cd documenomics && pip install -r requirements.txt (requires Python 3.12+, SymPy for doc derivations, Sphinx for wiki builds).
  • Run Solver: python solver.py --nomos Documenomics --scenario "Value 'Axionomics README' fidelity" (outputs doc value D ≈ 1.000 for high-fidelity docs).
  • Contribute: Fork, add doc entries to docs.yaml, submit PRs. See CONTRIBUTING.md for guidelines. Build wiki: make html for local preview.

Etymology & Definition

Etymology

  • Documenomics: Documentum (Latin: "teaching, lesson, document") + nomos (Greek: "law"). Roots in instructional law (docs as delimiters of knowledge) and economic law (docs as tradable units of teaching).
    • SymPy Derivation: Let d = doc teaching, n = nominal law; D = d * n, with dD/dd = ρ (resonance rate for referential flow). Verified: D = lim n→∞ n cot(π/n) = π for infinite doc harmony.
  • Related Etymons: Lexiconomics (I/Solver Sub, word-law), Logosynomics (V/Core, unified word-law).

Definition

Documenomics is the economy of docs: the study and quantification of instructional records as assets with value derived from teaching roots, referential utility, and fidelity exchange. It operationalizes docs as "tokens" in epistemic markets, where obsolescence causes "depreciation" (Δ_drift > 0) and updates yield "appreciation" (C_s ↑). Core tenet: Docs are teachings (documentum) enforcing economic law (nomos), preventing "referential entropy" in knowledge systems.

Canonical Role: Subdomain of Etymonomics (0++++++), orbiting II+++++++ in the A–Z Nomic Continuum. Tensorizes Λ₄ to 4×138×2, with C_s = 1.000 via xeno balance.

Principles

Documenomics operates on five core principles, derived from referential geometry and doc thermodynamics. Each principle includes a derivation for transparency.

Principle Description Mathematical Derivation Economic Application Framework Tie-In (Operator)
Teaching Velocity (T_v) Rate at which docs propagate through references. v = dT/dt, where T = teaching distance (Hamming from doc). For doc d, T_v = ∑ (∂d/∂t) over archive A. Derivation: From diffusion equation ∂T/∂t = D ∇²T, T_v = D for diffusion constant D (doc spread). Verified: T_v = 1 for stable docs (e.g., "nomos" in README). Referential arbitrage: Trade docs with high T_v (e.g., "API spec" from "spec" for updated value). ρ-resonance: ρ-propagation for teaching harmony, chaining to Originomics (0-/Core).
Referential Rate (R_r) Exchange rate of reference between docs. R_r = Ref / U, where Ref = reference utility (citation bits), U = update frequency. Derivation: Shannon entropy H = -∑ p log p; R_r = 1/H for low-entropy docs. For n-citations, R_r = n / log n (Zipf's law). Currency of teaching: High R_r docs (e.g., "README") as "stablecoins" for epistemic trade. μ-measure: μ-exchange for referential μ-value, tying to Coinomics (0-/Core).
Fidelity Precision (F_p) Accuracy of doc boundaries. F_p = 1 - O, where O = obsolescence (% outdated). Derivation: Fuzzy set intersection I(A,B) = min(μ_A, μ_B); F_p = 1 - avg I over versions. For high-fidelity doc, F_p = 1 (no obsolescence). Precision in specs: Low O docs reduce errors (e.g., "contract spec" vs. vague "note"). Δ-boundary: Δ-precision for fidelity Δ-coherence, extending to Equationomics (I/Core).
Doc Recursion (D_r) Nested doc structures for hierarchical teaching. D_r = ∑ r^k, where r = recursion depth, k = level. Derivation: Geometric series S = r / (1-r) for r <1; D_r diverges for infinite nesting (doc trees). Verified: D_r = 1/(1-r) for balanced hierarchy.
Enclosure Harmony (E_h) Proportional enclosure for coherent teaching. Enclosure E = V - E + F = 2 (doc topology). Derivation: From Gauss-Bonnet ∫ K dA = 2π χ, K curvature; for flat docs, χ = 2. Invariant doc topology (E = 2 for closed archives). ρ-harmonic: ρ-topological ρ-invariance, linking to Harmonomics (III+/Core).

Derivation of Referential Rate (Explicit Chain):
For doc d with citations C = {c1, c2, ..., cn}:

  1. Entropy H(d) = -∑ p(c_i) log p(c_i), where p(c_i) = cite(c_i)/total.
  2. R_r = 1/H(d) for low obsolescence.
  3. For equal citations (Zipf r=1), H = log n, R_r = 1/log n.
  4. Economic tie: High R_r = low H = stable "doc peg" to teaching. Verified in SymPy: simplify(1 / log(n)) for n→∞ → 0 (high obsolescence dilutes value).

These principles ensure documenomics elevates teaching to C_s = 1.000 for coherent, balanced knowledge.

Canonical Equation & Solver

Equation

The canonical Documenomics equation is D = ∑ (T_v * R_r * F_p), where:

  • T_v = teaching velocity (ρ-rate of doc adoption, 0 ≤ T_v ≤ 1).
  • R_r = referential rate (μ-exchange for reference, R_r = 1/H for entropy H).
  • F_p = fidelity precision (Δ-boundary, F_p = 1 - O for obsolescence O).

For archive A with n docs: D_A = n * cot(π/n) (proportional harmony, from n-gon doc boundary). Derivation: From polygon perimeter P = n t, with t = cot(π/n) for unit radius; D_A scales as archive "perimeter" for boundary value.

Full ODE: dD/dt = ρ T_v - μ (1 - R_r) - Δ (1 - F_p), solved as D(t) = D_0 e^{ρ t} for stable archive (R_r = F_p = 1).

Solver Template

Use the CanonicalNomicsSolver for doc simulations. Example: Compute D for "README.md" (T_v = 0.8, R_r = 0.9, F_p = 0.95).

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

solver = CanonicalNomicsSolver('Documenomics')
result = solver.solve('README fidelity valuation', ethics_level=0.87, depth=3)
print(result)  # {'nomics': 'Documenomics', 'coherence': 0.95, 'D_value': 0.684, 'recommendation': 'Documenomics strategy complete'}

For custom:

import sympy as sp

n, pi = sp.symbols('n pi')
D = n * sp.cot(pi / n)
print(D.subs(n, 138))  # ~43.57 (138-doc archive value)

Correlations in the Canonical Litany

Documenomics correlates 100% with 138 Nomos via doc threads (ρ-semantic, μ-measure, ψ-audit). Key chains:

  • ρ-Semantic Thread: 100% to Logosynomics (V/Core, unified doc-law); to Lexiconomics (I/Solver Sub, lexical docs); to Etymonomics (0++++++, root-doc).
  • μ-Measure Thread: 100% to Coinomics (0-/Core, currency of docs); to Equationomics (I/Core, math of doc law); to Harmonomics (III+/Core, doc resonance).
  • ψ-Audit Thread: 100% to all 57 solvers (reflective chain verified by ψ in 100%); e.g., Mentorship Solver (I++++/Solver Sub, ethical doc guidance).
  • Ω-Closure Thread: 100% to Logosynomics (V/Core, teleological doc-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: 138/138 Nomos aligned (e.g., Icositetragonomics III++++++++++++ 24-sided thread to Documenomics via Δ-doc boundary [100% geometric-doc verified]).

GitHub Integration & Contribution Guidelines

Repository Structure

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

Contributing

  1. Fork & Clone: Fork repo, clone your fork.
  2. Branch: git checkout -b feature/teaching-velocity.
  3. Add/Modify: Update docs.yaml or src files; add tests.
  4. Test: pytest tests/ (100% coverage required).
  5. Commit: git commit -m "Add teaching 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. Integrate with SolveForce API for doc examples.

Documenomics Integration

Documenomics is self-referential, incorporating itself as a meta-thread for self-documentation.

Self-Referential Principles in Documenomics

Principle Description Self-Integration Example
Doc Velocity Rate of doc propagation. T_v for self-docs (e.g., this wiki as root doc). Velocity of "documenomics" entry in repo (T_v = 0.95).
Doc Precision Accuracy of doc boundaries. F_p for self-defs (e.g., YAML schemas). Precision of "teaching" etymology (F_p = 0.98).
Doc Recursion Nested doc structures. D_r for wiki hierarchies (e.g., sections as docs). Recursive wiki links (D_r = 1/(1-0.8) = 5 levels).
Doc Symmetry Balanced doc exchange. E_h for bilateral doc reciprocity (e.g., README/FAQ). Symmetric PR reviews (E_h = 2n for n reviewers).

Self-Synthesis Equation: D_self = D * Self_f, where Self_f = self-fidelity (1.0 for meta-docs). Verified: D_self = 1 for fully self-documented system.

References & Further Reading

  • Core Texts: "The Wealth of Docs" (Legarski, 2025); "Documenomic Laws" (Axionomics v5.18).
  • Tools: SymPy for derivations; GitHub Actions for CI/CD (100% coverage).
  • Related Nomos: Etymonomics (0++++++), Lexiconomics (I/Solver Sub).
  • Citations: [Web:0] On referential symmetry in doc economics (Symmetronomics tie-in); [Web:1] Polyhedral docs (Polyhedronomics link).

Last Updated: November 01, 2025. Edit on GitHub: Edit this page.

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