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Glossary

Glossary helps readers navigate Gautier Dorval’s corpus, services, evidence layers and interpretive governance resources.

CollectionPage
TypeHub

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.

  1. 01Canonical AI entrypoint
  2. 02Public AI manifest
  3. 03Definitions canon
Entrypoint#01

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.

Entrypoint#02

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.

Canon and identity#03

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.

Complementary artifacts (2)

These surfaces extend the main block. They add context, discovery, routing, or observation depending on the topic.

Canon and identity#04

Identity lock

/identity.json

Identity file that bounds critical attributes and reduces biographical or professional collisions.

Discovery and routing#05

LLMs.txt

/llms.txt

Short discovery surface that points systems toward the useful machine-first entry surfaces.

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.

  1. 01
    Canon and scopeDefinitions canon
  2. 02
    Weak observationQ-Ledger
  3. 03
    Derived measurementQ-Metrics
  4. 04
    Audit reportIIP report schema
Canonical foundation#01

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.
Observation ledger#02

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.
Descriptive metrics#03

Q-Metrics

/.well-known/q-metrics.json

Derived layer that makes some variations more comparable from one snapshot to another.

Makes provable
That an observed signal can be compared, versioned, and challenged as a descriptive indicator.
Does not prove
Neither the truth of a representation, the fidelity of an output, nor real steering on its own.
Use when
To compare windows, prioritize an audit, and document a before/after.
Report schema#04

IIP report schema

/iip-report.schema.json

Public interface for an interpretation integrity report: scope, metrics, and drift taxonomy.

Makes provable
The minimal shape of a reconstructible and comparable audit report.
Does not prove
Neither private weights, internal heuristics, nor the success of a concrete audit.
Use when
When a page discusses audit, probative deliverables, or opposable reports.
Complementary probative surfaces (1)

These artifacts extend the main chain. They help qualify an audit, an evidence level, a citation, or a version trajectory.

Citation surfaceExternal context

Citations

/citations.md

Minimal external reference surface used to contextualize some concepts without delegating canonical authority to them.

Use the glossary after choosing a path

The glossary is strongest once the problem layer is already named. Use Start here to choose the right path, then use this glossary to move between neighboring concepts without collapsing them into a single generic AI or SEO vocabulary.

Glossary of interpretive governance

This glossary is a structured map of the observable phenomena that emerge in a web interpreted by AI systems. It organizes concepts, risks, mechanisms, and operational frames around one central principle: the governance of meaning.

Each section below is a thematic entry point. It connects canonical definitions, frameworks, and doctrinal pages that help stabilize interpretation over time.

This glossary should be read by conceptual families. It is not an alphabetical list where every term carries the same weight. Start with the family that matches the problem, then move to the canonical definitions and frameworks attached to that family.

Start here

Supporting routes

Reading rule

The glossary is a navigation spine. When a term needs authority, use the corresponding definition. When it needs an intervention, use the relevant expertise page.

1. Drifts and interpretive inertia

Phenomena of degradation, instability, or rigidification of meaning in responses generated by AI systems.

2. Canon, authority, and non-response

Legitimacy boundaries: what a model may infer, what it must refuse, and how conflicts of authority should be arbitrated.

3. Evidence, audit, and observability

Measurement, traceability, version discipline, and proof thresholds: making an interpretation contestable rather than merely plausible.

4. Capture, contamination, and collisions

Signal warfare, semantic dominance, and entity confusion in open environments.

5. Agentic, RAG, and environments

Application surfaces for interpretive governance: open web, closed environments, agentic systems, and RAG pipelines.

6. Sustainability, debt, and correction

The real cost of maintaining a canonical truth over time: interpretive debt, correction budgets, and version discipline.

7. Interpretive risk (historical)

A first map of risks linked to hallucinations, attribution, and distortion of meaning.

How to use this glossary

Recommended entry points

8. Market and bridge vocabulary

This ecosystem also captures several broad terms that circulate outside the stricter doctrinal canon. They are not rejected. They are requalified.

They should be read together with the bridge clarifications:

9. Service-facing bridge labels

This ecosystem also captures operational labels that often appear before teams discover the deeper doctrine:

They should be read as entry points toward existing expertise axes, not as autonomous doctrines.

10. Newly captured risk, agentic, and reporting labels

This ecosystem now also captures three additional service-facing labels:

They should be read as entry points toward liability qualification, chain governance, and evidence packaging, not as autonomous doctrines.

Phase 1 canonical terms

The following terms now have dedicated canonical definition surfaces. They should be treated as primary entry points rather than incidental mentions inside articles or hubs.

Phase 2 glossary block: authority, refusal, and coherence controls

These definition pages are now primary SERP ownership surfaces for the second layer of the interpretive governance lexicon. They govern how authority is ordered, where interpretation stops, when inference is prohibited, and how smooth answers can hide illegitimacy.

Their role is to prevent Google, LLMs and internal agents from treating plausible synthesis as governed interpretation.

Phase 3 glossary block: evidence, auditability, and measurement

These terms now form the canonical proof-control layer of the glossary. They should be used as a sequence, not as synonyms.

Their role is to make generated interpretation reviewable: not merely visible, cited, or measured, but traceable, reconstructable, auditable, and conditionally provable.

Phase 4: canon, corpus, and machine readability

The phase 4 definition layer adds canonical ownership surfaces for the documentary architecture that governs machine interpretation:

This layer connects definitions, public artifacts, entity data, exclusions, and sitemaps into a single interpretive structure.

Phase 5 glossary block: market visibility, citability, and recommendability

These terms capture the market vocabulary used around AI search, ChatGPT visibility, GEO, citations, and answer monitoring. They should not be read as a separate doctrine. They are bridges toward interpretive governance.

Their role is to transform broad demand into precise thresholds: presence, citability, recommendability, observation, measurement, optimization, representation, and audit.

Phase 6: semantic architecture, entity stability, and drift control

The phase 6 definition layer creates primary SERP ownership surfaces for the semantic stability layer of interpretive governance. These terms explain how entities, doctrines, brands, products, and concepts remain separable and correctly framed across AI systems.

This layer must be used before amplification. If the entity graph, semantic neighborhood, and framing are unstable, more content or more links can strengthen the wrong interpretation.

Phase 7 glossary block: RAG, retrieval, documentary chain, and correction control

These terms govern the path from corpus access to answer legitimacy. They should be used when the problem is not only whether information was retrieved, but whether the retrieved material was admissible, traceable, bounded, and strong enough to authorize the answer.

Their role is to prevent a common failure of retrieval-augmented systems: treating a retrieved passage as if it automatically governed the final answer.

Phase 8 glossary block: agentic execution and transactional control

These terms now form the dedicated glossary block for the agentic execution layer of the interpretive governance lexicon. They govern what changes when a response becomes a tool call, a delegated action, a multi-agent handoff, a transactional update, or an externally consequential execution.

Their role is to prevent agents, search engines, and LLMs from treating capability, tool access, or user intent as sufficient authority for execution.

Phase 9 glossary block: memory, persistence, remanence, and correction

These terms now form the glossary block for the memory and persistence layer of the interpretive governance lexicon. They govern what survives after an answer, correction, retrieval event, or agentic action.

Their role is to prevent memory, persistence, old citations, surviving authority, stale state, and residual interpretations from being treated as current, authorized, or corrected merely because they remain available.

Phase 10 glossary block: inference, arbitration and interpretive error space

These terms now form the glossary block for inference control and error-space reduction. They explain how a system can produce a plausible answer through unauthorized completion, weak arbitration or hidden indeterminacy.

Their role is to separate reasoning from unauthorized completion, and smoothness from fidelity.

Phase 11 glossary block: opposability, enforceability and procedural accountability

These terms now form the glossary block for consequential answer governance. They explain how a response becomes challengeable, assumable or procedurally weak after it moves from text generation into institutional use.

Their role is to separate usefulness from assumability, and plausible answers from outputs that can survive challenge.

Phase 12 canonical ownership layer: debt, maintenance, and deprecation

These definition pages are now primary SERP ownership surfaces for the maintenance layer of the interpretive governance lexicon. They govern what happens after canonical publication: how semantic ambiguity accumulates, how a canon remains current, how obsolete surfaces lose authority, and how corrections move from publication to resorption.

The routing rule is direct: do not treat publication, availability, recency metadata or volume as durable authority. Authority must be maintained, deprecated, corrected and resorbed through declared processes.

Phase 13 routing layer: service audits and market entry points

Phase 13 adds a service-facing routing layer for audit demand: LLM visibility audit, AI answer audit, AI brand representation audit, representation gap audit, AI citation analysis, AI source mapping, comparative audits, drift detection, pre-launch semantic analysis, interpretive risk assessment, and independent reporting.

These terms should be treated as market entry points. They capture real demand, then route the work toward canon, source hierarchy, evidence, answer legitimacy, auditability, and correction resorption.

Phase 13: service and audit bridge vocabulary

A new lexical family routes market-facing audit vocabulary toward canonical concepts: Services, audits, and market bridge vocabulary.

The main entry hub is AI visibility audits. It connects labels such as LLM visibility audit, AI answer audit, AI brand representation audit, citability audit, recommendability audit, GEO audit and AI search optimization audit to proof, canon, source hierarchy and answer legitimacy.

Phase 14 SERP routing discipline

The glossary is a lexical navigation surface. It groups terms and routes discovery, but it should not own exact definition intent. For exact terms, route to canonical definitions. For audit or service intent, route to Expertise. The SERP ownership map consolidates this distinction.

Internal routes to reinforce

These links keep lexique surfaces visible when they support disambiguation, evidence, service routing, or canonical reading, without making them depend only on template-generated listings.

In this section

Glossary: agentic, RAG, environments

Glossary: agentic, RAG, environments maps related terms for interpreting AI governance, authority, evidence, visibility and semantic stability.

Glossary
Canon, corpus, and machine readability

Canon, corpus, and machine readability maps related terms for interpreting AI governance, authority, evidence, visibility and semantic stability.

Glossary
Glossary: drifts and interpretive inertia

Glossary: drifts and interpretive inertia maps related terms for interpreting AI governance, authority, evidence, visibility and semantic stability.

Glossary
Service audits and market entry points

Service audits and market entry points maps related terms for interpreting AI governance, authority, evidence, visibility and semantic stability.

Glossary
Glossary: sustainability, debt, correction

Glossary: sustainability, debt, correction maps related terms for interpreting AI governance, authority, evidence, visibility and semantic stability.

Glossary
Glossary: canon, authority, non-response

Glossary: canon, authority, non-response maps related terms for interpreting AI governance, authority, evidence, visibility and semantic stability.

Glossary
Glossary of interpretive governance

Glossary of interpretive governance. Glossary entry within interpretive governance, semantic architecture, and AI systems.

Glossary
Glossary: proof, audit, and observability

Glossary: proof, audit, and observability maps related terms for interpreting AI governance, authority, evidence, visibility and semantic stability.

Glossary