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.
Public AI manifest
/ai-manifest.json
Structured inventory of the surfaces, registries, and modules that extend the canonical entrypoint.
- Governs
- Access order across surfaces and initial precedence.
- Bounds
- Free readings that bypass the canon or the published order.
Does not guarantee: This surface publishes a reading order; it does not force execution or obedience.
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.
AI citation audit scoring matrix
This matrix evaluates citation quality across six dimensions. It should be used after basic access and page availability have been checked.
| Dimension | 0 | 1 | 2 | 3 |
|---|---|---|---|---|
| Access | URL blocked or unstable | accessible with friction | accessible and renderable | accessible, canonical and preview-safe |
| Retrieval | not observed | observed weakly | observed across variants | stable across query cluster |
| Extraction | no useful passage | passage vague | passage usable | passage self-contained and scoped |
| Citation role | absent or contradictory | ornamental | supporting | governing |
| Fidelity | answer distorts claim | answer overextends claim | answer mostly preserves claim | answer preserves claim, scope and limits |
| Stability | one-off observation | inconsistent | repeated but variable | repeated across systems, prompts and time |
How to use the matrix
Score the claim-source pair, not the page in isolation. The same URL can score high for one claim and low for another.
Output
The audit output should identify the weakest dimension and assign a correction type: access correction, retrieval correction, passage correction, source hierarchy correction or fidelity correction.
Related routes
Use this matrix with the AI citation readiness checklist and the AI citation readiness audit.