docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거
Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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---
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id: wiki-2026-0508-prisons-and-self-correction
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title: Prisons and Self-Correction
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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: [Penitentiary System, Carceral Reform]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.85
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verification_status: applied
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tags: [criminology, justice, history, sociology]
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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: n/a
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---
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# Prisons and Self-Correction
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## 매 한 줄
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> **"매 1790s Quaker 의 penitence-as-cure 의 invention"**. 매 Eastern State Penitentiary (1829) 의 solitary-confinement model 매 "self-correction through silent reflection" → 매 Foucault (1975 Discipline & Punish) 의 critique → 매 2026 의 evidence-based recidivism reduction debate.
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## 매 핵심
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### 매 historical arc
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1. Pre-1790: corporal/capital punishment, public execution.
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2. 1790-1830: Quaker penitentiary (Pennsylvania system) — isolation + silence.
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3. 1830-1900: Auburn system — silent congregate labor.
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4. 1900s: rehabilitation ideal, parole.
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5. 1970s-: "tough on crime" backlash, mass incarceration (esp. US).
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6. 2010s-: evidence-based reform, Norway model (Halden), restorative justice.
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### 매 modern data
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- US incarceration rate: 매 ~600/100k (2024) — 매 highest among OECD.
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- Norway recidivism: 매 ~20% within 2y; US: 매 ~67%.
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- RAND meta-analysis: education programs 매 reduce recidivism 매 ~43%.
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### 매 응용
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1. Policy design (recidivism reduction, sentencing reform).
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2. Software (case management, predictive risk — see fairness debate).
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3. Restorative-justice programs.
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## 💻 패턴
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### Recidivism modeling (responsible)
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```python
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import pandas as pd
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from sklearn.linear_model import LogisticRegression
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from fairlearn.metrics import demographic_parity_difference
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df = pd.read_csv('release_cohort.csv')
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X = df[['age_at_release', 'prior_arrests', 'program_completed', 'employment_post']]
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y = df['rearrest_within_3y']
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model = LogisticRegression().fit(X, y)
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pred = model.predict(X)
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# fairness audit
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print('DP diff (race):', demographic_parity_difference(y, pred, sensitive_features=df['race']))
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```
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### Risk-tool transparency (COMPAS critique)
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```
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ProPublica 2016 audit:
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- Black defendants: 45% false-positive (predicted re-offend, didn't)
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- White defendants: 23% false-positive
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→ disparate-impact even when "race-blind"
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```
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### Halden Prison design principles (Norway)
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```
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1. Normalize: cell ≈ dorm room, common kitchens.
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2. Education + work as default activity.
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3. Short sentences (max 21y for most crimes).
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4. Officer-inmate ratio high; relational, not custodial.
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5. Pre-release housing transition.
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```
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### Restorative-justice circle script
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```
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1. Storytelling: harmed party speaks first.
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2. Acknowledgment: harm-doer reflects.
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3. Community impact discussion.
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4. Repair plan: agreed actions, timeline.
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5. Follow-up at 30/90 days.
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```
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### Education program ROI (RAND 2013)
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```
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Cost per inmate education: $1,400-1,744 / year
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Reduced recidivism savings: ~$5/$1 invested
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3-year recidivism: 43% reduction
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Reform policy design | Norway/Halden + restorative |
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| Recidivism prediction | Avoid black-box; favor interpretable + fairness audits |
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| Drug offenses | Treatment courts, not incarceration |
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**기본값**: Education + employment + housing transition + restorative practices.
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## 🔗 Graph
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- 변형: [[Restorative Justice]]
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## 🤖 LLM 활용
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**언제**: policy analysis, criminology discussion, fairness-aware ML in criminal justice.
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**언제 X**: 매 software-only topic (this is policy/sociology).
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## ❌ 안티패턴
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- **Black-box risk-assessment**: 매 unaudited disparate impact.
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- **Solitary as default**: 매 mental-health damage 의 evidence.
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- **Long sentences as deterrent**: 매 evidence weak; certainty > severity.
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## 🧪 검증 / 중복
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- Verified (Foucault — Discipline & Punish; Norway corrections white papers; RAND 2013 education meta-analysis; ProPublica COMPAS).
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- 신뢰도 A-.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Prisons & Self-Correction FULL content |
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