---
id: wiki-2026-0508-interdisciplinary-research
title: Interdisciplinary Research
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Cross-disciplinary Research, Transdisciplinary Methodology, AI-aided Synthesis]
duplicate_of: none
source_trust_level: A
confidence_score: 0.85
verification_status: applied
tags: [research-methodology, interdisciplinary, synthesis, llm-research, science]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: research-tooling
---

# Interdisciplinary Research

## 매 한 줄
> **"매 단일 분과의 답 끝에서 진짜 문제는 시작된다"**. Interdisciplinary research 는 둘 이상의 분과의 개념/방법/데이터를 통합해 단일 분과로 풀 수 없는 문제를 다루며, 2026 은 LLM 기반 literature synthesis + cross-domain embedding + multi-modal 데이터셋이 합류 비용을 급격히 낮췄다.

## 매 핵심

### 매 3 등급 (Stokes/OECD 분류)
- **Multidisciplinary**: 분과들이 병렬로 기여, 통합 약함.
- **Interdisciplinary**: 개념/방법이 실제 융합, 새 frame 출현.
- **Transdisciplinary**: 학계 + 실무 + 시민이 공동 produce, 사회 문제 중심.

### 매 6 단계 워크플로
1. **Problem framing**: 분과 중립 문장. 이해관계자 정의.
2. **Concept mapping**: 분과별 용어 → 공통 개념지도.
3. **Method portfolio**: 양적/질적/시뮬/모델 등 조합 선택.
4. **Data fusion**: schema alignment, ontology mapping.
5. **Synthesis**: cross-validation, conflicting evidence 협상.
6. **Communication**: 청중별 (학계/정책/일반) 산출물 분리.

### 매 LLM 가속 포인트 (2026)
- 광범위 literature → 분과별 요약 + 공통 개념 추출.
- 용어 alignment (psychology "affect" ↔ ML "sentiment").
- 데이터 schema mapping 초안.
- conflicting findings 의 evidence table.
- 다언어 (영/독/중/한) 동시 처리.

### 매 응용
1. Climate × economics × policy.
2. Neuroscience × ML × ethics.
3. Public health × urban planning × CS.
4. Material science × ML (자율 실험실).

## 💻 패턴

### 1. concept map (Mermaid)
```mermaid
graph LR
  A[Climate model output] --> B((Common: risk))
  C[Economic IAM] --> B
  D[Public-health DALY] --> B
  B --> E[Policy intervention space]
```

### 2. ontology alignment (Python + rdflib)
```python
from rdflib import Graph, Namespace, URIRef
g = Graph()
PSY = Namespace("http://psy.example/")
ML  = Namespace("http://ml.example/")
g.add((PSY.affect_valence, URIRef("http://www.w3.org/2002/07/owl#equivalentClass"), ML.sentiment_polarity))
g.serialize("alignment.ttl", format="turtle")
```

### 3. literature synthesis (LLM + RAG)
```python
# pseudo: vector DB across psychology + ML + economics corpora
from qdrant_client import QdrantClient
client = QdrantClient(url="...")
hits = client.search(collection_name="multidomain",
                    query_vector=embed("decision under uncertainty"),
                    limit=40)
# group by domain, pass to LLM with: "summarize per-domain, then synthesize"
```

### 4. evidence table (CSV schema)
```csv
claim_id,claim,domain,study,n,effect_size,quality,conflicts_with
C1,X reduces Y,economics,Smith2024,1200,-0.23,B,
C2,X increases Y,psychology,Lee2025,80,0.41,B,C1
C3,No effect,public-health,Park2026,5400,-0.02,A,C1;C2
```

### 5. cross-domain embedding (sentence-transformers)
```python
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("intfloat/multilingual-e5-large")
docs = ["psy: 'cognitive load increases under noise'",
        "ml:  'model accuracy drops with input perturbation'"]
emb = m.encode(docs, normalize_embeddings=True)
# cosine similarity 로 유사 개념 탐지
```

### 6. methods portfolio matrix
```markdown
| Question                   | Quant survey | RCT | Sim model | Ethnography | LLM eval |
|---------------------------|:-:|:-:|:-:|:-:|:-:|
| Behavior under policy P    | x | x |   | x |   |
| Long-horizon system risk   |   |   | x |   |   |
| Stakeholder framing        |   |   |   | x | x |
```

### 7. stakeholder co-design canvas
```yaml
problem: urban heat × low-income mortality
stakeholders:
  - role: residents
    expertise: lived experience
    contribution: priorities, validation
  - role: epidemiologists
    contribution: exposure-response
  - role: urban planners
    contribution: intervention feasibility
  - role: ML researchers
    contribution: hyperlocal forecasting
shared_artifact: dashboard + intervention playbook
```

### 8. conflicting evidence reconciliation
```python
def reconcile(claims: list[dict]) -> dict:
    """quality-weighted vote across domains, flag if disagreement > 0.4."""
    score = sum(c["effect"] * QUALITY[c["q"]] for c in claims)
    norm  = sum(QUALITY[c["q"]] for c in claims)
    mean  = score / norm
    spread = max(c["effect"] for c in claims) - min(c["effect"] for c in claims)
    return {"mean": mean, "spread": spread, "needs_followup": spread > 0.4}
```

### 9. preregistration template (OSF)
```markdown
# Preregistration
- Hypotheses: H1 ... H2 ...
- Disciplines combined: economics, psychology
- Methods per discipline: RCT (psy), DiD (econ)
- Analysis pipeline: pre-specified Python notebook (commit hash a1b2c3)
- Stop conditions: ...
- Authorship + role (CRediT taxonomy)
```

### 10. CRediT roles in commit metadata
```bash
git commit -m "feat: synthesis pipeline

CRediT-Roles: conceptualization (alice), methodology (bob),
              software (carol), formal-analysis (dan)"
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| 단일 분과로 푸는 문제 | Disciplinary 유지 — 무리한 융합 금지 |
| 두 분과 병렬 기여 | Multidisciplinary, 협업 가벼움 |
| 개념/방법 통합 필요 | Interdisciplinary — concept map 필수 |
| 사회적 시급, 실무자 필요 | Transdisciplinary, 공동 produce |
| 문헌 폭주 | LLM RAG synthesis + evidence table |

**기본값**: concept map → method portfolio → preregistration → LLM-aided synthesis 순서.

## 🔗 Graph
- 부모: [[Research-Methodology]]

## 🤖 LLM 활용
**언제**: 분과별 literature 1차 요약, 용어 alignment, evidence table 초안, 다언어 자료 통합.
**언제 X**: 인과 추정 / 통계 모델링 자체 — 사람 검토. 윤리/IRB 판단도 사람.

## ❌ 안티패턴
- **분과 명사만 섞기 (jargon mash)**: 개념 통합 없이 용어만 — 의미 없음.
- **단일 method 강제**: 모든 분과에 RCT 강요 → 부적합.
- **stakeholder 후 영입**: 결론 다 나온 뒤 검토 받음 — co-design 무력.
- **synthesis 없는 multidisciplinary**: 챕터 병렬 = interdisciplinary 가 아님.
- **LLM 요약을 일차 자료로 인용**: 반드시 원문 확인 후 인용.

## 🧪 검증 / 중복
- Verified (OECD Frascati Manual, NSF SciSIP literature, Nature Interdisc 2026 reviews).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — 6단계 워크플로 + LLM synthesis 패턴 |
