docs: integration tools — cross-reference graph, concept index, research digest
Codex-built tooling: cross-reference graph, concept index with build script, and research integrator that extracted 142 scholars, 175 bibliography items, 4 contradiction topics, and coverage maps for Paper 009 planning. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Mortdecai
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#!/usr/bin/env python3
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"""Build a concept index and glossary for the VIBECODE-THEORY corpus."""
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from __future__ import annotations
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import json
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import re
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from collections import defaultdict
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from dataclasses import dataclass
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[2]
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OUT_DIR = Path(__file__).resolve().parent
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@dataclass
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class Document:
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doc_id: str
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title: str
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path: Path
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text: str
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supersedes: str | None
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CONCEPTS = [
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{
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"name": "Vibe Coding",
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"aliases": ["vibe coding"],
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"introduced_in": "001",
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"status": "active",
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"related_concepts": ["Social-Cognitive Framework", "Mental Model Accuracy", "Meta-Skill Argument"],
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},
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{
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"name": "Social-Cognitive Framework",
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"aliases": ["social-cognitive framework", "vibe coding as social skill", "social-cognitive processes"],
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"introduced_in": "004",
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"status": "active",
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"related_concepts": ["Vibe Coding", "Mental Model Accuracy", "Adaptive Communication", "Collaboration Management"],
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},
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{
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"name": "Mental Model Accuracy",
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"aliases": ["mental model accuracy", "mental model"],
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"introduced_in": "001",
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"status": "active",
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"related_concepts": ["Social-Cognitive Framework", "Adaptive Communication", "Collaboration Management"],
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},
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{
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"name": "Adaptive Communication",
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"aliases": ["adaptive communication", "constraint calibration", "register matching"],
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"introduced_in": "001",
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"status": "active",
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"related_concepts": ["Social-Cognitive Framework", "Mental Model Accuracy", "Collaboration Management"],
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},
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{
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"name": "Collaboration Management",
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"aliases": ["collaboration management", "task decomposition", "trust calibration", "recovery"],
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"introduced_in": "001",
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"status": "active",
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"related_concepts": ["Social-Cognitive Framework", "Adaptive Communication", "Technical Foundation"],
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},
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{
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"name": "Technical Foundation",
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"aliases": ["technical foundation", "technical expertise"],
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"introduced_in": "001",
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"status": "active",
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"related_concepts": ["Vibe Coding", "Collaboration Management", "Meta-Skill Argument"],
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},
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{
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"name": "Neurodivergence Note",
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"aliases": ["neurodivergence note", "neurodivergence hypothesis"],
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"introduced_in": "001",
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"status": "open question",
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"related_concepts": ["Social-Cognitive Framework"],
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},
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{
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"name": "Shelf-Life Problem",
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"aliases": ["shelf-life problem", "shelf life problem"],
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"introduced_in": "003",
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"status": "active",
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"related_concepts": ["Meta-Skill Argument", "Infrastructure Threshold"],
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},
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{
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"name": "Meta-Skill Argument",
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"aliases": ["meta-skill argument", "meta-skill"],
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"introduced_in": "004",
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"status": "active",
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"related_concepts": ["Shelf-Life Problem", "Vibe Coding", "Social-Cognitive Framework"],
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},
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{
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"name": "Cognitive Surplus",
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"aliases": ["cognitive surplus", "surplus of cognition", "the cognitive surplus"],
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"introduced_in": "002",
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"status": "active",
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"related_concepts": ["Agricultural Parallel", "Cognition as a Commodity", "Automation Spiral"],
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},
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{
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"name": "Agricultural Parallel",
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"aliases": ["agricultural parallel", "agricultural analogy"],
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"introduced_in": "002",
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"status": "active",
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"related_concepts": ["Cognitive Surplus", "Green Revolution", "Feudal Internet", "Dependency Trap"],
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},
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{
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"name": "Dual Cognition Problem",
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"aliases": ["dual cognition problem", "the dual cognition problem"],
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"introduced_in": "002",
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"status": "active",
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"related_concepts": ["Cognitive Preference Shift", "Cognitive Atrophy", "Cognitive Surplus"],
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},
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{
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"name": "Cognitive Atrophy",
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"aliases": ["cognitive atrophy", "capability loss"],
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"introduced_in": "002",
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"status": "open question",
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"related_concepts": ["Dual Cognition Problem", "Cognitive Preference Shift", "Biological Ratchet"],
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},
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{
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"name": "Green Revolution",
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"aliases": ["green revolution"],
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"introduced_in": "002",
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"status": "active",
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"related_concepts": ["Agricultural Parallel", "Feudal Internet"],
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},
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{
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"name": "Feudal Internet",
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"aliases": ["feudal internet"],
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"introduced_in": "002",
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"status": "active",
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"related_concepts": ["Agricultural Parallel", "Dependency Trap", "Cognition as a Commodity"],
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},
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{
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"name": "Dependency Trap",
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"aliases": ["dependency trap", "future 3: the dependency trap"],
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"introduced_in": "002",
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"status": "active",
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"related_concepts": ["Feudal Internet", "Cognitive Atrophy", "Y2K Parallel"],
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},
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{
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"name": "Automation Spiral",
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"aliases": ["automation spiral"],
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"introduced_in": "003",
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"status": "active",
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"related_concepts": ["Cognitive Surplus", "Feedback Loop", "Master-Apprentice Parallel"],
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},
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{
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"name": "Cognitive Preference Shift",
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"aliases": ["cognitive preference shift", "preference shift"],
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"introduced_in": "003",
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"status": "active",
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"related_concepts": ["Dual Cognition Problem", "Cognitive Atrophy", "Biological Ratchet"],
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},
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{
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"name": "Cognition as a Commodity",
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"aliases": ["cognition as a commodity", "cognition-as-commodity framing"],
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"introduced_in": "005",
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"status": "active",
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"related_concepts": ["Cognitive Surplus", "Feudal Internet", "Information/Cognition Resource Hierarchy"],
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},
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{
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"name": "Y2K Parallel",
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"aliases": ["y2k parallel", "ai y2k moment", "y2k moment"],
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"introduced_in": "005",
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"status": "active",
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"related_concepts": ["Dependency Trap", "Infrastructure Threshold", "Cognitive Surplus"],
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},
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{
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"name": "Information/Cognition Resource Hierarchy",
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"aliases": ["information and cognition as resources", "resource hierarchy"],
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"introduced_in": "005",
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"status": "active",
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"related_concepts": ["Cognition as a Commodity", "Knowledge Unification"],
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},
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{
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"name": "Feedback Loop",
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"aliases": ["feedback loop"],
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"introduced_in": "006",
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"status": "active",
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"related_concepts": ["Automation Spiral", "Master-Apprentice Parallel", "Niche Construction"],
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},
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{
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"name": "Master-Apprentice Parallel",
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"aliases": ["master-apprentice parallel", "master-apprentice relationship"],
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"introduced_in": "006",
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"status": "active",
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"related_concepts": ["Feedback Loop", "Automation Spiral", "The Golem"],
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},
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{
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"name": "Niche Construction",
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"aliases": ["niche construction"],
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"introduced_in": "006",
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"status": "active",
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"related_concepts": ["Feedback Loop", "Recursion Observation"],
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},
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{
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"name": "Theological Thread",
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"aliases": ["theological thread"],
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"introduced_in": "006",
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"status": "active",
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"related_concepts": ["Prometheus", "Knowledge Unification", "Recursion Observation"],
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},
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{
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"name": "Recursion Observation",
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"aliases": ["recursion observation", "cosmological → biological → linguistic → computational"],
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"introduced_in": "006",
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"status": "open question",
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"related_concepts": ["Theological Thread", "Niche Construction", "Knowledge Unification"],
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},
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{
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"name": "Infrastructure Threshold",
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"aliases": ["infrastructure threshold"],
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"introduced_in": "007",
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"status": "active",
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"related_concepts": ["Biological Ratchet", "Premature Dependencies", "Y2K Parallel"],
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},
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{
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"name": "Premature Dependencies",
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"aliases": ["premature dependencies", "dependency waiting for its enabling technology"],
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"introduced_in": "007",
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"status": "active",
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"related_concepts": ["Infrastructure Threshold", "Biological Ratchet"],
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},
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{
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"name": "Biological Ratchet",
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"aliases": ["biological ratchet", "dependency ratchet", "ratchet thesis"],
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"introduced_in": "007",
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"status": "active",
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"related_concepts": ["Infrastructure Threshold", "Cognitive Preference Shift", "Knowledge Unification"],
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},
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{
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"name": "Dependency Chain",
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"aliases": ["dependency chain"],
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"introduced_in": "007",
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"status": "active",
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"related_concepts": ["Biological Ratchet", "Knowledge Unification", "Cheating Frame"],
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},
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{
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"name": "Knowledge Unification",
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"aliases": ["knowledge unification", "unification thesis", "unification of human knowledge", "the dependency chain as knowledge unification"],
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"introduced_in": "008",
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"status": "active",
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"related_concepts": ["Dependency Chain", "Singularity as Compilation", "Integration Layer"],
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},
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{
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"name": "Singularity as Compilation",
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"aliases": ["singularity as compilation", "compilation not transcendence", "compilation, not transcendence"],
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"introduced_in": "008",
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"status": "active",
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"related_concepts": ["Knowledge Unification", "Integration Layer", "Cheating Frame"],
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},
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{
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"name": "Integration Layer",
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"aliases": ["integration layer"],
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"introduced_in": "008",
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"status": "active",
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"related_concepts": ["Knowledge Unification", "Singularity as Compilation", "Existential Purpose of the Chain"],
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},
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{
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"name": "Ship of Theseus Problem",
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"aliases": ["ship of theseus problem", "identity problem", "species identity problem", "the identity problem"],
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"introduced_in": "008",
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"status": "active",
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"related_concepts": ["Continuity Argument", "Identity Argument", "Pragmatic Argument"],
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},
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{
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"name": "Continuity Argument",
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"aliases": ["continuity argument", "the continuity argument"],
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"introduced_in": "008",
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"status": "active",
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"related_concepts": ["Ship of Theseus Problem", "Identity Argument", "Pragmatic Argument"],
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},
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{
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"name": "Identity Argument",
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"aliases": ["identity argument", "essentialist", "the identity argument"],
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"introduced_in": "008",
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"status": "active",
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"related_concepts": ["Ship of Theseus Problem", "Continuity Argument", "Pragmatic Argument"],
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},
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{
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"name": "Pragmatic Argument",
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"aliases": ["pragmatic argument", "the pragmatic argument"],
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"introduced_in": "008",
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"status": "active",
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"related_concepts": ["Ship of Theseus Problem", "Continuity Argument", "Identity Argument"],
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},
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{
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"name": "Cheating Frame",
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"aliases": ["did we cheat", "cheating frame"],
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"introduced_in": "008",
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"status": "active",
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"related_concepts": ["Dependency Chain", "Singularity as Compilation", "Existential Purpose of the Chain"],
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},
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{
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"name": "Existential Purpose of the Chain",
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"aliases": ["existential purpose of the dependency chain", "existential purpose", "the existential purpose of the dependency chain"],
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"introduced_in": "008",
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"status": "active",
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"related_concepts": ["Integration Layer", "Cheating Frame", "Knowledge Unification"],
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},
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{
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"name": "Eve's Apple",
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"aliases": ["eve's apple"],
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"introduced_in": "eves-apple",
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"status": "reference allegory",
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"related_concepts": ["Cognitive Preference Shift", "Dependency Chain"],
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},
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{
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"name": "Pandora's Box",
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"aliases": ["pandora's box"],
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"introduced_in": "pandoras-box",
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"status": "reference allegory",
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"related_concepts": ["Dependency Chain", "Automation Spiral"],
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},
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{
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"name": "Prometheus",
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"aliases": ["prometheus"],
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"introduced_in": "prometheus",
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"status": "reference allegory",
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"related_concepts": ["Theological Thread", "Dependency Chain", "Cheating Frame"],
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},
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{
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"name": "Sorcerer's Apprentice",
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"aliases": ["sorcerer's apprentice"],
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"introduced_in": "sorcerers-apprentice",
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"status": "reference allegory",
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"related_concepts": ["Automation Spiral", "Feedback Loop", "Dependency Chain"],
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},
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{
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"name": "The Golem",
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"aliases": ["the golem", "golem"],
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"introduced_in": "the-golem",
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"status": "reference allegory",
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"related_concepts": ["Master-Apprentice Parallel", "Dependency Chain"],
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},
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{
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"name": "Faustian Bargain",
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"aliases": ["faustian bargain", "faust"],
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"introduced_in": "faust",
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"status": "reference allegory",
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"related_concepts": ["Feedback Loop", "Cognitive Preference Shift"],
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},
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{
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"name": "Icarus",
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"aliases": ["icarus"],
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"introduced_in": "icarus",
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"status": "reference allegory",
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"related_concepts": ["Shelf-Life Problem", "Infrastructure Threshold"],
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},
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{
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"name": "Tower of Babel",
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"aliases": ["tower of babel", "babel"],
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"introduced_in": "tower-of-babel",
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"status": "reference allegory",
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"related_concepts": ["Dependency Chain", "Knowledge Unification"],
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},
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]
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def clean_text(text: str) -> str:
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text = text.replace("\r\n", "\n")
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return text
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def load_documents() -> dict[str, Document]:
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docs: dict[str, Document] = {}
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for path in sorted(ROOT.glob("00*.md")):
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text = clean_text(path.read_text(encoding="utf-8"))
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title_match = re.search(r"^#\s+Paper\s+(\d{3}):\s*(.+)$", text, re.MULTILINE)
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supersedes_match = re.search(r"^\*\*Supersedes:\*\*\s*Paper\s+(\d{3})", text, re.MULTILINE)
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if not title_match:
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continue
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doc_id = title_match.group(1)
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docs[doc_id] = Document(
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doc_id=doc_id,
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title=title_match.group(2).strip(),
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path=path,
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text=text,
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supersedes=supersedes_match.group(1) if supersedes_match else None,
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)
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for path in sorted((ROOT / "allegorical").glob("*.md")):
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text = clean_text(path.read_text(encoding="utf-8"))
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title_match = re.search(r"^#\s+(.+)$", text, re.MULTILINE)
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doc_id = path.stem
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docs[doc_id] = Document(
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doc_id=doc_id,
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title=title_match.group(1).strip() if title_match else path.stem,
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path=path,
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text=text,
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supersedes=None,
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)
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return docs
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def paragraphs(text: str) -> list[str]:
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return [p.strip() for p in re.split(r"\n\s*\n", text) if p.strip()]
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def sentences(text: str) -> list[str]:
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normalized = re.sub(r"\s+", " ", text.strip())
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return [s.strip() for s in re.split(r"(?<=[.!?])\s+", normalized) if s.strip()]
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def alias_present(text: str, alias: str) -> bool:
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pattern = r"\b" + re.escape(alias.lower()) + r"\b"
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return re.search(pattern, text.lower()) is not None
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def extract_section(text: str, heading: str) -> str:
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pattern = rf"^##+\s+{re.escape(heading)}\s*$"
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match = re.search(pattern, text, re.MULTILINE | re.IGNORECASE)
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if not match:
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return ""
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start = match.end()
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next_heading = re.search(r"^##+\s+", text[start:], re.MULTILINE)
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end = start + next_heading.start() if next_heading else len(text)
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return text[start:end].strip()
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def first_matching_sentence(doc: Document, aliases: list[str]) -> str:
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for alias in aliases:
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section = extract_section(doc.text, alias)
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if section:
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for paragraph in paragraphs(section):
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if paragraph.startswith("#"):
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continue
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for sentence in sentences(paragraph):
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if len(sentence) >= 40:
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return sentence
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for paragraph in paragraphs(doc.text):
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if paragraph.startswith("#") or paragraph.startswith("**Authors:**") or paragraph.startswith("**Date:**"):
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continue
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for sentence in sentences(paragraph):
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if any(alias_present(sentence, alias) for alias in aliases) and len(sentence) >= 40:
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return sentence
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return "Definition sentence not found in source text."
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def find_mentions(docs: dict[str, Document], aliases: list[str]) -> list[str]:
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refs: list[str] = []
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for doc_id, doc in docs.items():
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if any(alias_present(doc.text, alias) for alias in aliases):
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refs.append(doc_id)
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||||
return refs
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||||
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||||
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def find_revisions(docs: dict[str, Document], concept: dict, mentions: list[str]) -> list[str]:
|
||||
introduced_in = concept["introduced_in"]
|
||||
revisions: list[str] = []
|
||||
for doc_id in mentions:
|
||||
doc = docs[doc_id]
|
||||
if doc.supersedes == introduced_in:
|
||||
revisions.append(doc_id)
|
||||
return sorted(revisions)
|
||||
|
||||
|
||||
def find_challenges(docs: dict[str, Document], concept: dict, mentions: list[str]) -> list[str]:
|
||||
aliases = [concept["name"]] + concept["aliases"]
|
||||
challenged: list[str] = []
|
||||
for doc_id in mentions:
|
||||
if doc_id == concept["introduced_in"]:
|
||||
continue
|
||||
doc = docs[doc_id]
|
||||
section = extract_section(doc.text, "Relationship to Prior Papers")
|
||||
open_q = extract_section(doc.text, "Open Questions") + "\n" + extract_section(doc.text, "Open Questions for Paper 007") + "\n" + extract_section(doc.text, "Open Questions for Paper 009")
|
||||
corpus = f"{section}\n{open_q}\n{doc.text[:3000]}"
|
||||
if any(alias_present(corpus, alias) for alias in aliases) and re.search(
|
||||
r"challenge|critic|rebuttal|unfalsif|weak|bounded|downgrade|unknown",
|
||||
corpus,
|
||||
re.IGNORECASE,
|
||||
):
|
||||
challenged.append(doc_id)
|
||||
return sorted(set(challenged))
|
||||
|
||||
|
||||
def mermaid_id(name: str) -> str:
|
||||
return "c_" + re.sub(r"[^a-z0-9]+", "_", name.lower()).strip("_")
|
||||
|
||||
|
||||
def build_index() -> dict[str, list[dict]]:
|
||||
docs = load_documents()
|
||||
items: list[dict] = []
|
||||
|
||||
for concept in CONCEPTS:
|
||||
aliases = [concept["name"]] + concept["aliases"]
|
||||
intro_doc = docs[concept["introduced_in"]]
|
||||
mentions = find_mentions(docs, aliases)
|
||||
revised_in = find_revisions(docs, concept, mentions)
|
||||
challenged_in = find_challenges(docs, concept, mentions)
|
||||
referenced_in = [doc_id for doc_id in mentions if doc_id != concept["introduced_in"] and doc_id not in revised_in]
|
||||
definition = first_matching_sentence(intro_doc, aliases)
|
||||
|
||||
items.append(
|
||||
{
|
||||
"name": concept["name"],
|
||||
"aliases": sorted(set(concept["aliases"])),
|
||||
"introduced_in": concept["introduced_in"],
|
||||
"definition": definition,
|
||||
"revised_in": revised_in,
|
||||
"challenged_in": challenged_in,
|
||||
"referenced_in": referenced_in,
|
||||
"status": concept["status"],
|
||||
"related_concepts": concept["related_concepts"],
|
||||
}
|
||||
)
|
||||
|
||||
return {"concepts": items}
|
||||
|
||||
|
||||
def write_glossary(index: dict[str, list[dict]]) -> None:
|
||||
lines = ["# VIBECODE-THEORY Glossary", ""]
|
||||
for item in sorted(index["concepts"], key=lambda x: x["name"].lower()):
|
||||
lines.extend(
|
||||
[
|
||||
f"## {item['name']}",
|
||||
f"Origin: {item['introduced_in']}",
|
||||
f"Status: {item['status']}",
|
||||
f"Aliases: {', '.join(item['aliases']) if item['aliases'] else 'None'}",
|
||||
item["definition"],
|
||||
f"Revised in: {', '.join(item['revised_in']) if item['revised_in'] else 'None'}",
|
||||
f"Challenged in: {', '.join(item['challenged_in']) if item['challenged_in'] else 'None'}",
|
||||
f"Referenced in: {', '.join(item['referenced_in']) if item['referenced_in'] else 'None'}",
|
||||
f"Related concepts: {', '.join(item['related_concepts']) if item['related_concepts'] else 'None'}",
|
||||
"",
|
||||
]
|
||||
)
|
||||
(OUT_DIR / "glossary.md").write_text("\n".join(lines), encoding="utf-8")
|
||||
|
||||
|
||||
def write_mermaid(index: dict[str, list[dict]]) -> None:
|
||||
lines = ["graph TD"]
|
||||
for item in index["concepts"]:
|
||||
lines.append(f' {mermaid_id(item["name"])}["{item["name"]}"]')
|
||||
seen: set[tuple[str, str]] = set()
|
||||
for item in index["concepts"]:
|
||||
for related in item["related_concepts"]:
|
||||
edge = tuple(sorted((item["name"], related)))
|
||||
if edge in seen:
|
||||
continue
|
||||
seen.add(edge)
|
||||
lines.append(
|
||||
f" {mermaid_id(item['name'])} -->|relates to| {mermaid_id(related)}"
|
||||
)
|
||||
(OUT_DIR / "concept_map.mermaid").write_text("\n".join(lines) + "\n", encoding="utf-8")
|
||||
|
||||
|
||||
def main() -> None:
|
||||
index = build_index()
|
||||
(OUT_DIR / "index.json").write_text(json.dumps(index, indent=2) + "\n", encoding="utf-8")
|
||||
write_glossary(index)
|
||||
write_mermaid(index)
|
||||
print(f"Indexed {len(index['concepts'])} concepts.")
|
||||
print(f"Wrote {OUT_DIR / 'index.json'}")
|
||||
print(f"Wrote {OUT_DIR / 'glossary.md'}")
|
||||
print(f"Wrote {OUT_DIR / 'concept_map.mermaid'}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user