Antigravity Agent
f8b21af4be
10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
143 lines
5.3 KiB
Markdown
143 lines
5.3 KiB
Markdown
---
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id: wiki-2026-0508-academic-integrity
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title: Academic Integrity
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Research Ethics, Scholarly Honesty, Plagiarism Policy]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [ethics, research, academia, ai-policy, plagiarism]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: N/A
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framework: ICAI/COPE guidelines
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---
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# Academic Integrity
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 ICAI fundamental value
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1. **Honesty** — 매 misrepresent 의 X.
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2. **Trust** — 매 peer 의 work 의 build on.
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3. **Fairness** — 매 equal standard.
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4. **Respect** — 매 prior work 의 cite.
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5. **Responsibility** — 매 own action 의 stand by.
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6. **Courage** — 매 misconduct 의 report.
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### 매 violation taxonomy
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- **Plagiarism**: 매 attribution 없이 idea/text 의 use.
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- **Fabrication**: 매 data 의 invent.
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- **Falsification**: 매 result 의 manipulate (image edit, p-hack).
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- **Authorship abuse**: ghost / gift / honorary author.
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- **Duplicate publication**: 매 same paper 의 multiple venue.
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- **Peer review breach**: 매 confidential manuscript 의 leak / scoop.
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### 매 LLM era (2026) 의 new question
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- **AI-assisted writing**: 매 disclosure required (Nature, Science, ICML 의 explicit policy 2024+).
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- **AI as author**: 매 prohibited (모든 major venue) — 매 accountability 의 absent.
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- **Code generation**: 매 LLM-generated code 의 review + test 의 author responsibility.
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- **Synthetic data**: 매 disclosure + provenance log.
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- **AI peer review**: 매 manuscript 의 LLM 의 upload 의 confidentiality breach (NeurIPS 2024 ban).
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### 매 응용
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1. Citation hygiene (DOI, BibTeX, persistent ID).
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2. Pre-registration (OSF, AsPredicted) 의 p-hack 방지.
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3. Code + data sharing (Zenodo, GitHub release w/ DOI).
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4. Conflict-of-interest 의 disclosure.
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5. AI-use statement (each paper).
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## 💻 패턴
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### AI-use disclosure block (2026 standard)
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```markdown
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## AI Tool Usage Statement
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- Claude Opus 4.7 was used for: prose editing, code review, literature summarization.
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- All scientific claims, experimental design, and analysis were verified by the authors.
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- Generated code was reviewed line-by-line and unit-tested.
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- No AI tool is listed as an author.
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```
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### Reproducibility checklist (NeurIPS-style)
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```yaml
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code: https://github.com/lab/proj # archived to Zenodo
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data: https://zenodo.org/record/XXXXX
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seeds: [0, 1, 2, 3, 4]
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hardware: 8x H100, 80GB
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runtime_per_run: 4h
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hyperparams: configs/main.yaml
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preregistration: https://osf.io/XXXXX
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```
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### Plagiarism / paraphrase detection
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```python
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# 매 simhash + embedding 의 hybrid
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from datasketch import MinHash
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def fingerprint(text: str, k: int = 5) -> MinHash:
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m = MinHash(num_perm=128)
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for i in range(len(text) - k + 1):
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m.update(text[i:i+k].encode())
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return m
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# 매 cosine sim of sentence embedding (>0.92) 의 secondary check
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```
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### Citation graph integrity
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```python
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import requests
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def verify_doi(doi: str) -> dict:
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r = requests.get(f"https://api.crossref.org/works/{doi}")
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r.raise_for_status()
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return r.json()["message"] # 매 fake DOI 의 fail 의 됨
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```
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### Pre-registration diff
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```bash
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# 매 pre-reg vs final manuscript 의 diff — exploratory vs confirmatory 의 separate
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diff prereg/hypothesis.md paper/section_3_hypothesis.md
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```
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## 매 결정 기준
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| 상황 | Practice |
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|---|---|
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| LLM 의 prose polish | Disclose, no co-author |
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| LLM 의 idea generation | Disclose, human verify each claim |
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| Synthetic / augmented data | Disclose generation method + seed |
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| Reproducing prior work | Cite, share repro code |
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| Negative result | Publish (preprint OK) — 매 file-drawer 의 anti |
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| Reviewer 의 LLM 의 use | Generally forbidden (check venue policy) |
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**기본값**: 매 transparent disclosure + 매 verifiable artifact (code/data/preregistration).
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## 🔗 Graph
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- 부모: [[Research Ethics]] · [[Scientific Method]]
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## 🤖 LLM 활용
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**언제**: 매 prose editing, literature summarization, code review — 매 disclosure 와 함께.
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**언제 X**: 매 peer review 의 manuscript upload, 매 ghostwrite 의 entire paper, 매 author listing.
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## ❌ 안티패턴
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- **Hidden LLM use**: 매 detection (perplexity, watermark) 의 risk + retraction.
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- **Citation laundering**: 매 not-read source 의 cite — 매 secondary citation chain bug.
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- **Salami slicing**: 매 one study 의 multiple paper 의 split — 매 venue policy violation.
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- **HARKing** (Hypothesizing After Results Known): 매 exploratory 의 confirmatory 의 disguise.
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- **P-hacking**: 매 multiple comparison 의 unreported.
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- **Image duplication**: 매 western blot reuse — 매 detection (ImageTwin, Proofig) 의 routine 2026.
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## 🧪 검증 / 중복
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- Verified (ICAI Fundamental Values 3rd ed 2021; COPE Core Practices; Nature AI policy 2023; NeurIPS 2024 reviewer guidelines).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — 2026 LLM-era policy + reproducibility patterns |
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