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.
Dual Web index
/dualweb-index.md
Canonical index of published surfaces, precedence, and extended machine-first reading.
- 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.
Canonical AI entrypoint
/.well-known/ai-governance.json
Neutral entrypoint that declares the governance map, precedence chain, and the surfaces to read first.
- 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
- 02Response authorizationQ-Layer: response legitimacy
- 03Evidence artifactinterpretation-policy.json
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-Layer: response legitimacy
/response-legitimacy.md
Surface that explains when to answer, when to suspend, and when to switch to legitimate non-response.
- Makes provable
- The legitimacy regime to apply before treating an output as receivable.
- Does not prove
- Neither that a given response actually followed this regime nor that an agent applied it at runtime.
- Use when
- When a page deals with authority, non-response, execution, or restraint.
interpretation-policy.json
/.well-known/interpretation-policy.json
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
The hidden weakness of context
Human readers are excellent at using context.
They see the site. They understand the page hierarchy. They notice a heading, a warning, a publication date, a domain, a disclaimer, or a neighboring paragraph. A large part of authority is carried by the environment around the statement.
AI systems can use context too. But they do not always preserve it.
The moment content becomes a retrievable fragment, context starts to leak.
Why structure matters
Structure is the part of context that can travel.
A source hierarchy can travel. A canonical URL can travel. A machine-readable policy can travel. A declared definition, relation, negation, and response-legitimacy rule can travel better than a page impression.
This is the core difference between human publishing and machine interpretation.
For humans, context may be enough.
For AI systems, context must be converted into structure if authority is expected to survive.
Dual Web implication
This is one reason the Dual Web matters.
The human-facing layer can explain, persuade, nuance, and teach.
The machine-first layer must disambiguate, prioritize, bound, negate, route, and suspend.
Those two layers do not replace each other. They carry different parts of authority.
The risk of inferred authority
When structure is missing, the system does not stop interpreting. It infers authority from weaker signals: salience, wording, ranking, frequency, or apparent expertise.
That may produce a plausible answer. It may also misplace the source that should govern the answer.
Closing rule
In human publishing, authority is often carried by context. In machine interpretation, authority must be carried by structure.