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.
- 01Weak observationQ-Ledger
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.
Domain strength can help a source become visible. It does not prove that the source is legitimate for the claim being made.
This distinction is essential in AI citation analysis. Answer systems may select strong, well-known or frequently linked domains because they are easy to retrieve and appear trustworthy. But the strongest domain is not always the right authority.
Authority is claim-specific
A review site may be legitimate for user sentiment and illegitimate for the official service perimeter. A news article may be legitimate for an event and illegitimate for the current state. A marketplace may be legitimate for availability and illegitimate for product governance. An official doctrine page may be legitimate for definitions but irrelevant for customer reviews.
Legitimacy is not a property of the domain alone. It is a relationship between source, claim, scope and decision context.
Why domain authority can mislead
High-authority domains can dominate source selection because they rank well, are frequently cited and carry strong external signals. This can create source substitution: the system relies on a prominent external source when the canonical source should govern.
The answer may look credible because the cited domain is credible. Yet credibility at the domain level does not authorize every claim inside every context.
Source legitimacy as a stricter test
Source legitimacy asks whether the source has the right role to constrain the answer. The test includes proximity, freshness, authority, scope, source type and relation to the entity.
A legitimate source does not need to be the most powerful domain. It needs to be the right source for the claim.
Practical rule
When auditing AI citations, do not rank sources only by domain strength. Classify them by governing capacity. Ask: which source should own this claim? Which source only illustrates it? Which source is outdated, derivative or secondary?
That shift turns citation analysis from a popularity exercise into an authority map.