Governance artifacts
Governance files brought into scope by this page
This page is anchored to published surfaces that declare identity, precedence, limits, and the corpus reading conditions. Their order below gives the recommended reading sequence.
Definitions canon
/canon.md
Canonical surface that fixes identity, roles, negations, and divergence rules.
- Governs
- Public identity, roles, and attributes that must not drift.
- Bounds
- Extrapolations, entity collisions, and abusive requalification.
Does not guarantee: A canonical surface reduces ambiguity; it does not guarantee faithful restitution on its own.
Site context
/site-context.md
Notice that qualifies the nature of the site, its reference function, and its non-transactional limits.
- Governs
- Editorial framing, temporality, and the readability of explicit changes.
- Bounds
- Silent drifts and readings that assume stability without checking versions.
Does not guarantee: Versioning makes a gap auditable; it does not automatically correct outputs already in circulation.
Evidence layer
Probative surfaces brought into scope by this page
This page does more than point to governance files. It is also anchored to surfaces that make observation, traceability, fidelity, and audit more reconstructible. Their order below makes the minimal evidence chain explicit.
- 01Canon and scopeDefinitions canon
- 02Weak observationQ-Ledger
Definitions canon
/canon.md
Opposable base for identity, scope, roles, and negations that must survive synthesis.
- Makes provable
- The reference corpus against which fidelity can be evaluated.
- Does not prove
- Neither that a system already consults it nor that an observed response stays faithful to it.
- Use when
- Before any observation, test, audit, or correction.
Q-Ledger
/.well-known/q-ledger.json
Public ledger of inferred sessions that makes some observed consultations and sequences visible.
- Makes provable
- That a behavior was observed as weak, dated, contextualized trace evidence.
- Does not prove
- Neither actor identity, system obedience, nor strong proof of activation.
- Use when
- When it is necessary to distinguish descriptive observation from strong attestation.
A system may cite or reconstruct a source because it appears known, not because the current page legitimately supports the answer.
Known-source risk is one of the most important limits of citation analysis. A cited URL can look authoritative while the underlying source behavior is unstable, outdated or reconstructed.
The visible citation may not be the real proof path.
What known-source risk means
Known-source risk appears when a system treats a source, URL pattern, domain or page type as familiar and uses that familiarity to support an answer without enough current verification.
This can happen when an old URL remains known, when a site changed its structure, when a brand used to publish a claim it no longer supports, or when a model reconstructs a plausible source path from memory-like patterns.
Phantom citation
A phantom citation is a citation that appears to point to a source but cannot be verified as a current, supporting, accessible source for the claim being made.
It may be a broken URL, a reconstructed URL, an outdated page, a page that never contained the claim, or a citation whose displayed source does not support the answer.
Why this matters for AI citation readiness
Citation readiness is not only about getting cited. It is also about preventing the wrong source from becoming the apparent authority.
A corpus can be damaged by old pages, archived claims, inconsistent slugs, copied descriptions, weak redirects and abandoned content that still looks plausible to answer systems.
If those surfaces remain ungoverned, a model may cite the past version of the entity rather than the current one.
Typical symptoms
| Symptom | Risk |
|---|---|
| AI cites a retired URL | The answer may be governed by a stale source |
| AI names a page that does not exist | The system may be reconstructing a plausible path |
| AI cites a page but uses another claim | The citation may be ornamental or mismatched |
| AI uses an old brand description | Entity memory may override current positioning |
| AI cites an aggregator | A derivative source may replace the canonical source |
How to reduce the risk
The correction is not simply “publish more content”. The correction is to stabilize the current source environment.
Useful actions include:
- mapping old URLs and redirect behavior;
- clarifying which pages are current and canonical;
- removing or marking obsolete claims;
- creating self-contained current passages;
- strengthening entity consistency;
- linking market-facing pages back to stricter definitions;
- monitoring citations by role, not only by count.
Governance implication
Known-source risk is a source hierarchy problem. The system may know a source, but knowing a source does not make it admissible authority for the present claim.
An audit must therefore ask whether the cited source is current, accessible, relevant, supporting and authorized to govern the answer. If not, the citation is not evidence of fidelity. It is a symptom requiring correction.