Acceptable Use Policy
Acceptable Use Policy 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 sets the practical rules for using the platform without treating outputs as professional advice or guaranteed predictions.
Scope
Allowed learning use, prohibited high-stakes reliance, harmful-use boundaries, affiliate disclosure, and shared footer governance links.
Implementation Status
Version 2.0 makes the policy testable through risk-boundary audits and link-safety verification.
This policy governs the permitted uses of all astrology content (Zi Wei Dou Shu and Numerology) and plain-language analysis services on starnum.com.tw. By using this platform, you agree to comply with this policy.
1. Positioning of Astrology Content
All astrology content on this site is a cultural self-exploration tool that provides perspective on personality tendencies, energetic traits, and life themes. It is not:
- Medical diagnosis, treatment advice, or alternative therapy
- Legal advice or financial investment advice
- Mental-health treatment or counseling
- A scientifically verifiable predictive system
2. Permitted Uses
- Personal-character exploration and self-understanding
- Understanding interpersonal relationships (family, friendship, work partners)
- Supplementary reference for career orientation and talent discovery
- Cultural research and academic citation (please credit the source)
- Entertainment-purpose astrology reading
- A supporting tool used alongside a qualified mental-health counselor
- Educational purposes (studying traditional East Asian metaphysical culture)
3. Prohibited Uses
3.1 Strictly Forbidden (platform will take immediate removal or blocking action)
- Crime prediction：Using astrology information to predict or accuse any person of criminal tendencies or moral character
- Discriminatory use：Discriminating against individuals in employment, housing, lending, or any other form, based on their chart or numerology
- Manipulating others：Using astrology readings to create fear, anxiety, or psychological manipulation
- Defamation and attacks：Reading real individuals' (including public figures') charts and using them as a tool for attack
- Minors：Providing personal astrology analysis to children under 13 without guardian consent
- Superstitious intimidation：Using threatening language such as "disaster is inevitable" or "you must change your fate immediately"
- Fraudulent purposes：Using site content for monetary scams, spiritual-healing sales, or false claims
3.2 Requires Extra Caution (user responsibility)
- Over-relying on astrology results for major life decisions (marriage, investment, surgery)
- Using astrology readings as a reason to refuse professional medical help
- Sharing another person's chart information without their consent
4. Guidelines for AI-Assisted Generated Content
Some plain-language interpretations on this site are AI-assisted and reviewed by humans. When quoting or sharing such AI-assisted astrology content, users should:
- Clearly mark it as cultural astrology reference, not a factual statement
- Not use it to impersonate a human astrologer's personal advice
- Credit the original source (starnum.com.tw) when quoting
5. Enforcement
The site reserves the right to take the following actions against users who violate this policy:
- Removal of violating content
- Restriction or termination of service access
- Cooperation with legal investigations when necessary
6. Policy Updates
This policy may be updated as the service evolves. Material changes will be announced on @mychenan, and the version date at the top of the page will be updated.
7. Contact
For any questions about this policy, please contact us via Instagram @mychenan.
External standards and primary sources
These primary sources inform this page. They are benchmarks, not third-party endorsements of this site.
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/public-claim-registry.jsondata/ai-answer-readiness-audit.jsondata/state-machine/trust-pages.json- Related governance pages: Privacy Policy · Ethics Statement · AI Safety · FAQ
- 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 |
| claim.acceptable-use.risk-boundary-coverage Acceptable-use risk-boundary coverage |
{"articles":7976,"aiAnswerReady":7976,"riskBoundaryArticles":2239,"failures":0} | verified | acceptable-use | node scripts/verify-public-evidence.js --check |
| claim.acceptable-use.provider-use-boundary Provider alignment is not a production model claim |
{"unsupportedProductionModelClaimsBlocked":true,"rule":"Named frontier models are not treated as production usage unless code/config evidence exists.","benchmarkProviders":["OpenAI | verified | acceptable-use | node scripts/verify-public-evidence.js --check |
| claim.acceptable-use.footer-link-safety Footer link safety audit |
{"pages":180,"pass":180,"fail":0} | verified | acceptable-use | node scripts/verify-trust-pages.js --check |
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 |