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Research v2.0Version 2.0 · · Governance 2.0 public evidence surface
Governance 2.0 Overview
This page is part of the starnum public Governance 2.0 surface and uses the same evidence layer as the system card, data governance, transparency report, use policy, and security policy.
Governance Summary
This page documents the research operating model behind cultural interpretation, chart methodology, content quality, multilingual translation quality, and AI-assisted governance workflows.
Scope
Astrology-method notes, true-solar-time assumptions, knowledge-base and translation-memory lineage, AWR/TM quality gates, benchmark references, and public transparency artifacts.
Implementation Status
Version 2.0 turns the research page into a maintained evidence hub: claims point to machine-readable artifacts, while systems detect and agents decide whether content needs repair.
집필 관점:陸斌兆派를 분석 기준으로 삼고 다른 유파의 차이를 객관적으로 비교합니다. 유파 간 우열은 판단하지 않습니다.
업데이트 주기:매월 1편 자동 발행 (스크립트:
scripts/publish-research.js ) · RSS
陸斌兆派 사화 해석 차이: 戊·庚·壬 천간의 세 가지 관점
자미두수에서 가장 논란이 많은 지점 중 하나입니다. 戊 천간에서 우필화과인가 천기화과인가? 庚에서는 태음인가 천동인가? 壬에서는 좌보화록인가 천량화록인가? 본문은 陸斌兆派, 王亭之派, 欽天四化를 문헌 근거와 함께 정리합니다.
명리 지식베이스에 적용한 GraphRAG: 성요에서 궁위까지의 의미 그래프
총 2,758 檔 1538K 행의 명리 지식베이스를 Neo4j 그래프 (343 節點 / 679 關係) 로 전환하고 Qdrant 벡터 검색과 결합해 삼중 증거 융합 기반 명반 분석 시스템을 구축한 과정입니다.
117 條 명리 하드 룰 설계: 사화 "불가능 사건"의 경계 정의
명리 AI의 진짜 난관은 생성력이 아니라 "명리적으로 불가능한 사건"을 말하지 않게 막는 것입니다. 본문은 117 條 개 하드 룰 (四化 41 + 格局 49 + 核心指標 17) 의 설계 논리와 실제 사례를 설명합니다.
생명 수비학 3×3 그리드의 화살표: 대만파와 서양 피타고라스 체계의 차이
대만에서 널리 쓰이는 "구궁격 수비학"과 서양 Pythagorean 체계는 Arrow Lines 정의에서 분명한 차이를 보입니다. 두 체계의 계산 방식을 정리하고 본 사이트가 채택한 기준과 그 이유를 설명합니다.
외부 표준 및 1차 자료
다음 1차 자료는 이 페이지의 판단 기준입니다. 비교 기준일 뿐 제3자가 이 사이트를 보증한다는 뜻은 아닙니다.
Current Machine Audit Snapshot
This block uses only traceable local audit data. No unsupported metrics or model claims are added.
- data/state-machine/i18n-parity.json: 8,036 parent URLs, 7,976 articles.
- data/kb-machine-audit.json: 3,238 source files, 0 missing coverage, 0 orphan chunks.
- data/discovery-surface-audit.json: 0 errors, 0 warnings.
- data/sla-report.json: critical / 5 critical, 0 warnings.
Content Maintenance And Update Decision
This block makes governance-page content machine-checkable: every page must disclose its source artifacts, related pages, and the gate that reports update needs.
Update Decision
This is not static copy. When source artifacts, related policies, public metrics, or generators change, AI Ops reports evidence and an AI agent decides whether the page needs edits.
Human Boundary
Systems detect, report, and preserve machine-readable evidence. Codex/Claude agents perform final judgment and repair.
Verification Command
node scripts/verify-trust-pages.js --check
data/state-machine/public-bench.jsondata/kb-machine-audit.jsondata/public-claim-registry.json- Related governance pages: methodology · benchmark · System Card · Transparency Report
- Update flow:
npm run update:trust-pages→npm run test:trust
Verifiable Evidence Layer
This block is not a narrative claim. Each core assertion has a claim id, source JSON, hash, and a repeatable verification command. Public pages disclose governance evidence without exposing source code, secrets, private data, or exploitable attack details.
| Claim ID | Verifiable value | Status | Owner | Source and verification |
|---|---|---|---|---|
| claim.public-url-manifest.indexable-count Public URL and canonical inventory |
38,965 indexable URLs | verified | sitewide | node scripts/generate-public-evidence-manifest.js --dry |
| claim.trust-pages.audit-pass-rate Trust page machine audit |
180/180 pass | verified | sitewide | node scripts/verify-trust-pages.js --check |
| claim.discovery-surface.zero-errors AI discovery surface audit |
{"errors":0,"warnings":0} | verified | sitewide | node scripts/verify-discovery-surface.js |
| claim.structured-data.jsonld-errors JSON-LD / structured data audit |
{"structured_data_invalid_files":0,"breadcrumb_count":28274,"faq_count":27506,"dataset_count":30,"article_count":27406} | verified | sitewide | node scripts/site-machine-audit.js |
| claim.status.sla-state Status page SLA source |
critical / 5 critical, 0 warnings | verified | sitewide | node scripts/generate-status-page.js |
| claim.provider-alignment.openai-anthropic-gemini OpenAI / Anthropic / Google Gemini benchmark alignment |
benchmark alignment only unless code/config evidence exists | verified | sitewide | node scripts/verify-public-evidence.js --check |
| claim.transparency-report.sha256 Transparency report SHA-256 anchor |
{"report":"transparency/report-2026-Q3.json","sha256":"47b09e2ca4e8b8fe9dffdfaccef3b11212de9ee3a8a14badca8044e2481203c5"} | verified | sitewide | node scripts/update-transparency-current-data.js |
| claim.release-integrity.gpg-signing GPG signing status |
GPG signing configured locally; GitHub verification pending | github_verification_pending | sitewide | gpg --list-secret-keys --keyid-format=long && git log -1 --show-signature |
System Card V2.0: Technical Transparency Layer
This layer publishes the technical governance evidence that can be safely disclosed: architecture, data sources, AI-use boundaries, quality gates, release integrity, and provider alignment. Source code, secrets, exploitable attack details, and private data remain out of scope.
Public architecture
Cloudflare Pages/Workers, R2/D1/KV/Pagefind, and local generation scripts form the public-site and governance publication chain. Public pages disclose behavior, state, and traceable sources, not secrets or internal permissions.
AI-use disclosure
AI-assisted workflows are used for knowledge-base retrieval, cross-checking, and error detection. Governance documents are benchmarked against OpenAI, Anthropic, and Google Gemini public frameworks. Production model usage is disclosed only when code/config evidence exists.
Quality and safety gates
Governance page audit 180/180 passing, JSON-LD errors 0, discovery-surface errors 0. Status pages report critical / 5 critical, 0 warnings as-is.
Data traceability
Knowledge base 32,724 chunks, TM 789,031 entries, AI answer-ready 7,976/7,976. Public metrics trace to data/state-machine/*, data/*audit*.json, and transparency reports.
| Governance area | OpenAI | Anthropic | Google Gemini | Starnum implementation evidence |
|---|---|---|---|---|
| Model/system-card disclosure | OpenAI models + safety docs | Claude model docs + system/model cards | Gemini model docs + safety settings | system-card, model-card, methodology, benchmark, transparency-log |
| Safety evaluation and use boundaries | Safety best practices / deployment checklist | Responsible Scaling / safety policy | Gemini safety controls / policy | AI safety, acceptable-use, ethics, risk-boundary copy, crawler policy audit |
| Data governance | Data controls / privacy controls | privacy and data handling docs | Gemini API data governance references | privacy, ai-data-governance, KB/TM source tracking, SHA-256 hashes |
| Monitoring and release | production checklist / eval discipline | system-card transparency discipline | model/version documentation discipline | deploy.js, status.html, SLA report, trust-pages-machine-audit, sitemap/hreflang audits |
- Sources: data/state-machine/model-card.json, public-bench.json, trust-pages.json, security-headers.json.
- Sources: data/trust-pages-machine-audit.json, data/discovery-surface-audit.json, data/ai-answer-readiness-audit.json.
- Sources: data/kb-machine-audit.json, data/tm/quality-audit-report.json, data/sla-report.json.
- Official benchmark docs checked: 2026-07-30; links are listed in the OpenAI / Anthropic / Google Gemini alignment table.
The V2.0 goal is not more claims; it separates implemented controls from planned controls. Production usage, benchmark alignment, status exceptions, GPG signing, and SLA breaches are disclosed from source data.
Release Integrity And GPG
GPG signing configured locally. signingkey=0934DFA0EDA6363A. GitHub verification pending until the public key upload and Verified badge are confirmed.
OpenAI / Anthropic / Google Gemini Alignment
The governance surface is benchmarked against the three public frameworks: model docs, system/model cards, safety evaluation, data governance, and use policies. This is benchmark alignment, not a claim that every provider is active in production inference. Official docs checked: 2026-07-30
| Provider | Governance focus | Starnum disclosure | Official source |
|---|---|---|---|
| OpenAI | Model documentation, latest model notes, safety best practices, and data controls. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://platform.openai.com/docs/models |
| Anthropic | Claude model documentation, system/model cards, Responsible Scaling, and safety policy. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://docs.anthropic.com/en/docs/about-claude/models |
| Google Gemini | Gemini API model documentation, safety settings, data governance, and platform policy. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://ai.google.dev/gemini-api/docs/models |