---
id: wiki-2026-0508-academic-integrity
title: Academic Integrity
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Research Ethics, Scholarly Honesty, Plagiarism Policy]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [ethics, research, academia, ai-policy, plagiarism]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: N/A
  framework: ICAI/COPE guidelines
---

# Academic Integrity

## 매 한 줄
> **"매 honest attribution + reproducible claim"**. Academic integrity = 매 work 의 origin (idea, code, data, prose) 의 truthful disclosure + 매 method 의 reproducibility. 2026 LLM-pervasive era 의 매 redefined — 매 "who wrote it" 의 less important, 매 "what was verified" 의 central.

## 매 핵심

### 매 ICAI fundamental value
1. **Honesty** — 매 misrepresent 의 X.
2. **Trust** — 매 peer 의 work 의 build on.
3. **Fairness** — 매 equal standard.
4. **Respect** — 매 prior work 의 cite.
5. **Responsibility** — 매 own action 의 stand by.
6. **Courage** — 매 misconduct 의 report.

### 매 violation taxonomy
- **Plagiarism**: 매 attribution 없이 idea/text 의 use.
- **Fabrication**: 매 data 의 invent.
- **Falsification**: 매 result 의 manipulate (image edit, p-hack).
- **Authorship abuse**: ghost / gift / honorary author.
- **Duplicate publication**: 매 same paper 의 multiple venue.
- **Peer review breach**: 매 confidential manuscript 의 leak / scoop.

### 매 LLM era (2026) 의 new question
- **AI-assisted writing**: 매 disclosure required (Nature, Science, ICML 의 explicit policy 2024+).
- **AI as author**: 매 prohibited (모든 major venue) — 매 accountability 의 absent.
- **Code generation**: 매 LLM-generated code 의 review + test 의 author responsibility.
- **Synthetic data**: 매 disclosure + provenance log.
- **AI peer review**: 매 manuscript 의 LLM 의 upload 의 confidentiality breach (NeurIPS 2024 ban).

### 매 응용
1. Citation hygiene (DOI, BibTeX, persistent ID).
2. Pre-registration (OSF, AsPredicted) 의 p-hack 방지.
3. Code + data sharing (Zenodo, GitHub release w/ DOI).
4. Conflict-of-interest 의 disclosure.
5. AI-use statement (each paper).

## 💻 패턴

### AI-use disclosure block (2026 standard)
```markdown
## AI Tool Usage Statement
- Claude Opus 4.7 was used for: prose editing, code review, literature summarization.
- All scientific claims, experimental design, and analysis were verified by the authors.
- Generated code was reviewed line-by-line and unit-tested.
- No AI tool is listed as an author.
```

### Reproducibility checklist (NeurIPS-style)
```yaml
code: https://github.com/lab/proj  # archived to Zenodo
data: https://zenodo.org/record/XXXXX
seeds: [0, 1, 2, 3, 4]
hardware: 8x H100, 80GB
runtime_per_run: 4h
hyperparams: configs/main.yaml
preregistration: https://osf.io/XXXXX
```

### Plagiarism / paraphrase detection
```python
# 매 simhash + embedding 의 hybrid
from datasketch import MinHash
def fingerprint(text: str, k: int = 5) -> MinHash:
    m = MinHash(num_perm=128)
    for i in range(len(text) - k + 1):
        m.update(text[i:i+k].encode())
    return m
# 매 cosine sim of sentence embedding (>0.92) 의 secondary check
```

### Citation graph integrity
```python
import requests
def verify_doi(doi: str) -> dict:
    r = requests.get(f"https://api.crossref.org/works/{doi}")
    r.raise_for_status()
    return r.json()["message"]  # 매 fake DOI 의 fail 의 됨
```

### Pre-registration diff
```bash
# 매 pre-reg vs final manuscript 의 diff — exploratory vs confirmatory 의 separate
diff prereg/hypothesis.md paper/section_3_hypothesis.md
```

## 매 결정 기준
| 상황 | Practice |
|---|---|
| LLM 의 prose polish | Disclose, no co-author |
| LLM 의 idea generation | Disclose, human verify each claim |
| Synthetic / augmented data | Disclose generation method + seed |
| Reproducing prior work | Cite, share repro code |
| Negative result | Publish (preprint OK) — 매 file-drawer 의 anti |
| Reviewer 의 LLM 의 use | Generally forbidden (check venue policy) |

**기본값**: 매 transparent disclosure + 매 verifiable artifact (code/data/preregistration).

## 🔗 Graph
- 부모: [[Research Ethics]] · [[Scientific Method]]

## 🤖 LLM 활용
**언제**: 매 prose editing, literature summarization, code review — 매 disclosure 와 함께.
**언제 X**: 매 peer review 의 manuscript upload, 매 ghostwrite 의 entire paper, 매 author listing.

## ❌ 안티패턴
- **Hidden LLM use**: 매 detection (perplexity, watermark) 의 risk + retraction.
- **Citation laundering**: 매 not-read source 의 cite — 매 secondary citation chain bug.
- **Salami slicing**: 매 one study 의 multiple paper 의 split — 매 venue policy violation.
- **HARKing** (Hypothesizing After Results Known): 매 exploratory 의 confirmatory 의 disguise.
- **P-hacking**: 매 multiple comparison 의 unreported.
- **Image duplication**: 매 western blot reuse — 매 detection (ImageTwin, Proofig) 의 routine 2026.

## 🧪 검증 / 중복
- Verified (ICAI Fundamental Values 3rd ed 2021; COPE Core Practices; Nature AI policy 2023; NeurIPS 2024 reviewer guidelines).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — 2026 LLM-era policy + reproducibility patterns |