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.
Freshness is not automatically better than stability. The correct question is whether the claim is time-sensitive, canonical, obsolete or still valid.
AI citation discussions often treat freshness as a universal advantage. That is too simple. Some claims require current data. Others require stable definitions. Some pages should change frequently. Others should remain intentionally stable so that systems can identify a durable source of meaning.
The risk is to update everything without governing what changed.
Four freshness regimes
A citation audit should classify claims by freshness regime.
| Regime | Example | Citation risk |
|---|---|---|
| Time-sensitive | pricing, schedule, regulation, availability | outdated citation |
| Versioned | policy, methodology, specification | wrong version cited |
| Evergreen | definition, doctrine, stable concept | unnecessary churn weakens stability |
| Transitional | rebrand, migration, service change | old and new states collide |
The same update strategy cannot govern all four regimes.
Freshness without scope creates drift
Updating a page can help if the previous state is obsolete. But updates can also create ambiguity if they do not declare what changed, what remains valid, and which older claims are no longer authoritative.
For AI-mediated systems, this matters because older content can remain retrievable through search indexes, third-party summaries, training memory, caches or citations. A new page does not automatically erase an old interpretation.
This is why known-source risk and phantom citation are linked to freshness. The system may believe it knows a source or URL even when the current site has moved on.
Citation stability is the stronger metric
Freshness asks whether the source is current. Citation stability asks whether the right source keeps governing the right claim across time, systems and prompt variants.
A page that is cited once after an update has not proven stability. A page that is repeatedly cited for the right claim, with the right role and without substitution, has stronger evidence.
This is why citation persistence and citation fidelity should be measured together.
Practical correction
For time-sensitive claims, expose dates, update cadence, validity windows and canonical current pages. For versioned claims, expose version numbers, effective dates and supersession routes. For evergreen doctrine, avoid unnecessary rewrites that weaken semantic continuity. For transitional states, create explicit bridges between old and new terminology.
A good page does not simply say “updated.” It says what the update governs.
Governance implication
Freshness becomes useful only when it is connected to source hierarchy. Without hierarchy, a system may cite the newest weak source instead of the strongest governing source. With hierarchy, the system has a clearer route: current operational claims go to current pages, definitions go to stable definitions, proof claims go to proof artifacts, and obsolete states are marked as such.
The goal is not maximal novelty. The goal is controlled temporal authority.