Blog de Investigación
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 research and evaluation context for cultural interpretation, content quality, and AI-assisted workflows.
Scope
Methodology notes, benchmark references, quality audits, knowledge-base coverage, and transparency artifacts.
Implementation Status
Version 2.0 links research claims to public evidence and separates benchmark references from production usage claims.
Postura editorial:Tomamos la escuela Lu Binzhao como referencia y comparamos otras escuelas con objetividad — sin establecer rangos entre ellas.
Frecuencia:Una publicación al mes, programada mediante
scripts/publish-research.js · RSS
Las Cuatro Transformaciones en la Escuela Lu Binzhao: Tres Lecturas de los Tallos Wu, Geng y Ren
Uno de los puntos más discutidos del Zi Wei Dou Shu: en el tallo Wu, ¿Youbi se transforma en Ke o lo hace Tianji? En Geng, ¿es Taiyin o Tiantong? En Ren, ¿Zuofu Lu o Tianliang Lu? Este artículo recorre en paralelo las escuelas Lu Binzhao, Wang Tingzhi y el linaje Qintian Sihua, con referencias a las fuentes.
GraphRAG Aplicado a una Base de Conocimiento Astrológica: Un Grafo Semántico de Estrellas a Palacios
Cómo convertimos 2,758 檔 1538K líneas de conocimiento astrológico en un grafo de Neo4j (343 節點 / 679 關係) y lo integramos con la búsqueda vectorial de Qdrant para construir un sistema de fusión de evidencia en tres capas.
117 條 Reglas Estrictas para la Lógica Astrológica: Definir los Límites de los Eventos Sihua Imposibles
El mayor reto de una IA astrológica no es la generación — es impedir que afirme sucesos astrológicamente imposibles. Este artículo explica cómo definimos 117 條 reglas estrictas (四化 41 + 格局 49 + 核心指標 17) con ejemplos reales.
Las Líneas de Flecha en la Cuadrícula 3×3 de la Numerología: Tradición Taiwanesa vs. Pitagórica
La "numerología de cuadrícula 3×3" popular en Taiwán y el sistema Pitagórico occidental definen las Arrow Lines de manera bastante distinta. Este artículo expone ambos métodos de cálculo y explica qué estándar elegimos — y por qué.
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: 3,231 source files, 0 missing coverage, 0 orphan chunks.
- data/discovery-surface-audit.json: 0 errors, 0 warnings.
- data/sla-report.json: critical / 2 critical, 0 warnings.
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 |
27,634 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 / 2 critical, 0 warnings | verified | sitewide | node scripts/generate-status-page.js |
| claim.provider-alignment.openai-anthropic-gemini OpenAI / Anthropic / Google Gemini benchmark alignment |
production evidence: claude-sonnet-4-5-20250514 | verified | sitewide | node scripts/verify-public-evidence.js --check |
| claim.transparency-report.sha256 Transparency report SHA-256 anchor |
{"report":"transparency/report-2026-Q2.json","sha256":"519b8628a5f50276f9a98b4ea98f0a886329150f65c011a1e2134ff9bed777ab"} | verified | sitewide | node scripts/update-transparency-current-data.js |
| claim.release-integrity.gpg-signing GPG signing status |
GPG signing active locally; checked GitHub commit verification is valid | verified | 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/Pagefind, Supabase, 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 / 2 critical, 0 warnings as-is.
Data traceability
Knowledge base 32,690 chunks, TM 529,820 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: 2026-05-26; 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 active. signingkey=0934DFA0EDA6363A. Checked GitHub commit verification is valid.
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: 2026-05-26
| 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 |