Antigravity Agent
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155 lines
6.1 KiB
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155 lines
6.1 KiB
Markdown
---
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id: wiki-2026-0508-activism
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title: Activism
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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: [Advocacy, Social Movement, Civic Engagement, Tech Activism]
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duplicate_of: none
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source_trust_level: B
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confidence_score: 0.85
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verification_status: applied
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tags: [activism, ethics, social-movement, civic-tech, ai-ethics]
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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: civic-tech tooling
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---
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# Activism
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## 매 한 줄
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> **"매 organized action toward social/political change"**. Activism = 매 collective effort 의 power structure 의 challenge — 매 protest, lobbying, boycott, mutual aid, civic-tech 의 spectrum. 2026 의 매 algorithmic platform + AI policy 의 central battleground 의 emerge — 매 EU AI Act, US executive order, labor union (Hollywood SAG-AFTRA AI clause) 의 outcome 의 activism-driven.
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## 매 핵심
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### 매 tactic spectrum
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- **Institutional**: lobbying, policy comment, litigation (EFF, ACLU model).
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- **Direct action**: protest, strike, blockade, boycott.
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- **Mutual aid**: 매 community 의 direct support — 매 state 의 bypass.
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- **Cultural**: art, media, narrative shift.
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- **Digital / civic-tech**: open data, FOIA tooling, mapping, OSINT.
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### 매 movement lifecycle (Stages of a Social Movement, Blumer 1969)
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1. **Emergence** — 매 unrest, scattered grievance.
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2. **Coalescence** — 매 leadership + identity.
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3. **Bureaucratization** — 매 formal org.
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4. **Decline** — 매 success / repression / co-optation / mainstream.
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### 매 tech-activism domain (2026 hot)
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- **AI ethics**: bias audit, dataset transparency, algorithmic accountability.
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- **Labor**: gig worker union (Uber, Doordash), tech worker walkout (Google AI ethics 2018+, OpenAI ex-staff).
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- **Privacy**: surveillance pushback (Pegasus, Clearview), e2e encryption defense.
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- **Climate tech**: Tech Won't Build It, Stop Cop City.
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- **Open source**: copyleft, fair-source debate, AI-output licensing.
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- **Disinformation**: fact-check infra, election integrity.
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### 매 응용
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1. Coalition building (cross-org alliance).
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2. Narrative campaign (framing, messaging).
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3. Policy intervention (model regulation comment, e.g. NIST AI RMF).
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4. Whistleblowing infra (SecureDrop, Signal).
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5. Crisis mapping (Ushahidi, Bellingcat OSINT).
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## 💻 패턴
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### Secure communication (org-internal)
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```bash
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# 매 Signal — 매 default for activist coordination
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# 매 disappearing message + safety number verify + screen-lock
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# 매 Wire / Element (Matrix) 의 self-host alternative
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```
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### FOIA / public records request (US)
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```python
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# muckrock-style template
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import requests
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req = {
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"agency_id": 123,
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"title": "Records re: facial recognition vendor contracts 2024-2026",
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"documents_requested": "All contracts, MOUs, RFPs related to FRT...",
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"fee_waiver_request": True, # public interest
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}
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# muckrock.com API 의 submit 또는 direct email per agency
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```
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### OSINT verification (geolocation)
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```python
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# 매 image metadata + reverse geocode + sun-shadow angle
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from PIL import Image
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from PIL.ExifTags import GPSTAGS, TAGS
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img = Image.open("photo.jpg")
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exif = img._getexif() or {}
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gps = next((v for t, v in exif.items() if TAGS.get(t) == "GPSInfo"), {})
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# 매 cross-ref Sentinel-2 satellite imagery + OpenStreetMap
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```
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### Mutual aid request board (simple)
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```python
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# Flask + sqlite — 매 community fridge / ride share / bail fund
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@app.post("/request")
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def post_request():
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db.execute("INSERT INTO need (kind, area, contact, ts) VALUES (?, ?, ?, ?)",
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(request.form["kind"], request.form["area"], hashed(request.form["contact"]), now()))
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```
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### Algorithmic audit (bias)
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```python
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from sklearn.metrics import confusion_matrix
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def disparate_impact(y_pred, y_true, group):
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rates = {}
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for g in set(group):
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mask = group == g
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rates[g] = (y_pred[mask] == 1).mean()
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return min(rates.values()) / max(rates.values()) # 매 <0.8 의 80% rule violation
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```
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### Petition / call-tool (5Calls-style)
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```ts
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// 매 lookup rep by zip → display script → log call
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const reps = await fetch(`/api/reps?zip=${zip}`).then(r => r.json());
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// reps: [{ name, phone, party, district }]
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```
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## 매 결정 기준
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| 상황 | Tactic |
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|---|---|
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| Policy window open (bill in committee) | Lobbying + public comment + coalition letter |
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| Corporate harm, public-facing | Boycott + media campaign + shareholder resolution |
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| Acute injustice (police violence) | Protest + legal observers + bail fund |
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| Long-term cultural shift | Narrative / art / education |
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| Tech worker concern | Internal organizing + ethical board + walkout (last resort) |
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| State surveillance | Encryption advocacy + legal (EFF model) |
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**기본값**: 매 multi-tactic + 매 sustained (movement >> moment) + 매 affected community 의 leadership.
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## 🔗 Graph
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- 부모: [[Civic Engagement]]
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- 응용: [[Algorithmic Accountability]]
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- Adjacent: [[AI Ethics]]
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## 🤖 LLM 활용
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**언제**: 매 policy doc summarization, 매 outreach draft, 매 large-corpus FOIA review.
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**언제 X**: 매 surveillance target identification, 매 deepfake disinfo generation, 매 confidential source data 의 cloud LLM 의 upload.
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## ❌ 안티패턴
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- **Slacktivism**: 매 hashtag 의 only — 매 sustained organizing 의 X.
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- **Voluntourism**: 매 affected community 의 lead-not 의 outsider 의 dominate.
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- **Burnout culture**: 매 unsustainable pace — 매 movement 의 collapse.
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- **OPSEC fail**: 매 plain-text channel, 매 metadata leak — 매 source 의 endanger.
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- **Performative ally**: 매 statement 의 only without resource shift.
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- **Astroturfing**: 매 fake grassroots — 매 trust 의 destroy 의 long-term.
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- **Single-tactic monoculture**: 매 only protest 또는 only lobby — 매 multi-vector 의 effective.
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## 🧪 검증 / 중복
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- Verified (Blumer 1969 *Collective Behavior*; Tarrow *Power in Movement* 3rd ed; EFF / Access Now annual reports; AI Now Institute reports 2018-2025).
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- 신뢰도 B (매 normative + tactical mix — context-dependent).
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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 — 2026 tech-activism focus + civic-tech patterns |
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