refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-principles-of-structuralism
+title: Principles of Structuralism
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Structuralism, Structural Analysis]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [philosophy, linguistics, methodology, semiotics]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: theory
+  framework: structural-analysis
+---
+
+# Principles of Structuralism
+
+## 매 한 줄
+> **"매 meaning emerges from relations, not essences."**. 매 Saussure 의 1916 *Cours de linguistique générale* 에서 출발한 사상으로, 매 element 의 의미는 그 자체가 아닌 system 내 다른 element 와의 차이 (difference) 로부터 도출된다는 매 framework. 매 2026 에서도 NLP embedding space, knowledge graphs, software architecture 의 modular decomposition 에 이르기까지 매 살아있는 분석 도구.
+
+## 매 핵심
+
+### 매 4대 원칙
+- **Synchrony over diachrony**: 매 system 의 현재 상태를 분석 — 매 historical evolution 보다 우선.
+- **Sign = signifier + signified**: 매 sound-image 와 concept 의 arbitrary pairing.
+- **Value through difference**: 매 "cat" 의 의미는 "bat", "rat", "hat" 와 다르기에 존재.
+- **Langue vs parole**: 매 underlying system (langue) vs 매 individual utterance (parole).
+
+### 매 확장 영역
+- **Lévi-Strauss (anthropology)**: 매 myths 의 binary oppositions (raw/cooked, nature/culture).
+- **Barthes (semiotics)**: 매 mythologies, 매 cultural codes, denotation vs connotation.
+- **Lacan (psychoanalysis)**: 매 unconscious 가 language 처럼 구조화되어 있다.
+- **Piaget (cognitive)**: 매 mental schemas 의 structural development.
+
+### 매 응용
+1. NLP embedding: 매 word2vec/GloVe 는 distributional structuralism 의 신경적 구현.
+2. Software architecture: 매 module 의 의미는 dependency graph 내 위치로 결정.
+3. UX semiotics: 매 icon affordance 는 매 visual sign system 내 차이로 해독.
+
+## 💻 패턴
+
+### Pattern 1: Distributional embedding (NLP)
+```python
+# 매 word meaning = 매 context distribution (distributional structuralism)
+import numpy as np
+from collections import Counter, defaultdict
+
+def build_cooccurrence(corpus, window=5):
+    cooc = defaultdict(Counter)
+    for sent in corpus:
+        for i, w in enumerate(sent):
+            for j in range(max(0, i-window), min(len(sent), i+window+1)):
+                if i != j:
+                    cooc[w][sent[j]] += 1
+    return cooc
+
+# 매 차이 — 두 word vector 사이의 cosine distance
+def diff(v1, v2):
+    return 1 - np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
+```
+
+### Pattern 2: Binary opposition extraction (Lévi-Strauss style)
+```python
+def extract_oppositions(text_units, embed_fn):
+    embeddings = [embed_fn(t) for t in text_units]
+    # 매 most-distant pairs = 매 strongest oppositions
+    pairs = []
+    for i in range(len(text_units)):
+        for j in range(i+1, len(text_units)):
+            d = np.linalg.norm(embeddings[i] - embeddings[j])
+            pairs.append((d, text_units[i], text_units[j]))
+    pairs.sort(reverse=True)
+    return pairs[:10]
+```
+
+### Pattern 3: Sign decomposition (Barthes)
+```typescript
+type Sign = {
+  signifier: string;        // 매 form (word, image, sound)
+  signified: string;        // 매 mental concept
+  denotation: string;       // 매 literal
+  connotation: string[];    // 매 cultural associations
+};
+
+const rose: Sign = {
+  signifier: "rose",
+  signified: "flower",
+  denotation: "Rosa genus plant",
+  connotation: ["love", "passion", "England", "secrecy (sub rosa)"],
+};
+```
+
+### Pattern 4: Structural diff for software modules
+```python
+# 매 module value = 매 dependency-graph position
+import networkx as nx
+
+def structural_role(g: nx.DiGraph, node):
+    return {
+        "in_degree": g.in_degree(node),
+        "out_degree": g.out_degree(node),
+        "betweenness": nx.betweenness_centrality(g).get(node, 0),
+        "neighbors": list(g.neighbors(node)),
+    }
+```
+
+### Pattern 5: Synchronic vs diachronic analysis
+```python
+def synchronic_snapshot(repo, commit_sha):
+    # 매 freeze a moment, analyze structure
+    return {"deps": parse_deps(repo, commit_sha)}
+
+def diachronic_trace(repo, sha_list):
+    # 매 evolution over time
+    return [synchronic_snapshot(repo, sha) for sha in sha_list]
+```
+
+### Pattern 6: Code review — surface vs deep structure
+```python
+# 매 surface (parole) — actual code
+# 매 deep (langue) — design pattern, architectural rule
+def review(pr):
+    surface = lint_results(pr)
+    deep = check_pattern_compliance(pr, patterns=["DI", "SRP", "boundary"])
+    return surface, deep
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| 매 "what does X mean?" | Map relations, not essences |
+| 매 NLP embedding choice | Distributional methods (word2vec, BERT) |
+| 매 cultural artifact analysis | Binary oppositions + connotations |
+| 매 software module design | Structural role > implementation detail |
+| 매 LLM prompt design | Define by contrast (few-shot oppositions) |
+
+**기본값**: 매 always ask "what is this *not*?" before "what is this?".
+
+## 🔗 Graph
+- 변형: [[Distributional Semantics]]
+- 응용: [[Word Embeddings]] · [[Knowledge Representation]] · [[Software Architecture]]
+
+## 🤖 LLM 활용
+**언제**: 매 meaning analysis, 매 cultural decoding, 매 embedding interpretation, 매 dependency graph reasoning.
+**언제 X**: 매 essentialist questions ("what is the *true* nature of X?") — 매 structuralism 은 reject 함.
+
+## ❌ 안티패턴
+- **Essentialism**: 매 "X has an inherent meaning" — 매 structuralism rejects this.
+- **Static langue**: 매 langue 를 fixed 로 보면 변화하는 system 을 놓침.
+- **Over-binarization**: 매 모든 것을 binary opposition 으로 환원하면 nuance 손실.
+- **Ignoring parole**: 매 actual usage data 무시하면 model 이 stale.
+
+## 🧪 검증 / 중복
+- Verified (Saussure 1916, Lévi-Strauss 1958, Barthes 1957).
+- 신뢰도 A (foundational philosophical canon).
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — Saussure 4대 원칙, NLP embedding 연결, 6 패턴 |