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
- 02Evidence artifactsite-context.md
- 03Evidence artifactai-manifest.json
- 04Evidence artifactai-governance.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.
site-context.md
/site-context.md
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.
ai-manifest.json
/ai-manifest.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.
ai-governance.json
/.well-known/ai-governance.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.
Complementary probative surfaces (2)
These artifacts extend the main chain. They help qualify an audit, an evidence level, a citation, or a version trajectory.
entity-graph.jsonld
/entity-graph.jsonld
Published surface that contributes to making an evidence chain more reconstructible.
llms.txt
/llms.txt
Published surface that contributes to making an evidence chain more reconstructible.
Machine-first canon
This page is the canonical definition of machine-first canon within the canon, corpus, and machine readability layer of interpretive governance.
A machine-first canon is a canonical layer written so machines can identify the authoritative identity, concepts, exclusions, source hierarchy, reading conditions, and non-inference rules of a corpus.
Short definition
A machine-first canon is a canonical layer written so machines can identify the authoritative identity, concepts, exclusions, source hierarchy, reading conditions, and non-inference rules of a corpus.
Why it matters
It is the stabilizing layer between the public doctrine and the systems that will compress, summarize, cite, or operationalize that doctrine.
In AI search, retrieval-augmented generation, autonomous browsing, and agentic reading, a corpus is not interpreted only by its visible prose. It is interpreted through routes, files, metadata, exclusions, entity relations, sitemap placement, and internal links. Machine-first canon names one part of that documentary control layer.
The strategic function is therefore not cosmetic. The concept helps prevent systems from flattening doctrine, service language, proof artifacts, and observations into the same authority level. It also gives search engines a clearer canonical page to associate with the term rather than forcing them to choose between a hub, a category, a blog article, and a machine artifact.
What it is not
It is not a hidden instruction layer, not a prompt, not a crawler manipulation tactic, and not a substitute for visible human-facing canon.
This distinction matters because machine-readable governance can create false confidence. A structured file, a definition page, or a graph relation should never be treated as proof that external systems comply with the intended reading. It only makes the intended reading more explicit, testable, and auditable.
Common failure modes
- machine files contradict human pages;
- machine files list concepts without explaining their perimeter;
- the canon is updated but the public artifacts lag behind;
- a system treats the machine-first canon as an offer, method, or compliance guarantee;
These failures are typical when the human corpus and the machine-first corpus evolve separately. They increase interpretive risk because models can still produce coherent answers while violating the source hierarchy or ignoring exclusions.
Governance implication
The machine-first canon must be synchronized with definitions, governance pages, public artifacts, and sitemaps. It should make explicit what is canonical, what is supportive, what is excluded, and what should not be inferred.
For SERP ownership, the same principle applies: the canonical page should receive descriptive links, appear in the definitions registry, be discoverable from the glossary, and be reinforced by machine-first artifacts without competing against them.
Related canonical definitions
- Canonical source
- Machine readability
- Machine-first artifacts
- AI manifest
- AI governance JSON
- Reading conditions
- Global exclusions