Visual schema
Implementation chain
A framework converts doctrine into protocol, then method, then usable instrumentation.
Doctrine
What must hold inside the frame.
Framework
The intermediate operational frame.
Protocol
Sequence or discipline of application.
Measurement
Observability, score, audit, proof.
Usage
Concrete deployment in an environment.
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.
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.
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.
Q-Metrics JSON
/.well-known/q-metrics.json
Descriptive metrics surface for observing gaps, snapshots, and comparisons.
- Governs
- The description of gaps, drifts, snapshots, and comparisons.
- Bounds
- Confusion between observed signal, fidelity proof, and actual steering.
Does not guarantee: An observation surface documents an effect; it does not, on its own, guarantee representation.
Complementary artifacts (3)
These surfaces extend the main block. They add context, discovery, routing, or observation depending on the topic.
Q-Ledger JSON
/.well-known/q-ledger.json
Machine-first journal of observations, baselines, and versioned gaps.
Definitions canon
/canon.md
Canonical surface that fixes identity, roles, negations, and divergence rules.
Q-Layer in Markdown
/response-legitimacy.md
Canonical surface for response legitimacy, clarification, and legitimate non-response.
Choose the failure mode first
The frameworks are easier to use when the failure mode is already identified. Start here separates visibility, authority, evidence, retrieval, memory and execution paths before sending the reader to the framework layer.
Frameworks and applicable frameworks
Public registry of frameworks derived from the interpretive governance doctrine developed by Gautier Dorval.
This page serves as an internal linking hub to connect operational frameworks (/frameworks/) to canonical concepts (/definitions/) and doctrine (/doctrine/). Each framework is an application surface: it makes mechanisms usable, auditable, and enforceable.
For the lexical registry, see Definitions and canonical concepts. For the doctrinal table, see Doctrine. For field analyses, see Interpretive phenomena.
Link hierarchy for frameworks
Frameworks should be selected by failure mode, not by title alone. A framework is useful when the reader already has a problem to diagnose, a corpus to structure, a response condition to enforce or a correction chain to maintain.
Start here
- Response legitimacy: Q-Layer governance of response conditions and Enforceable response conditions for AI agents.
- Retrieval and source control: RAG governance, retrieval and inference control and RAG governance vs interpretive governance.
- Entity and graph stability: Entity collisions and the interpretive graph and Exogenous governance: external graph stabilization.
- Evidence and observability: Interpretive observability: metrics, logs, evidence and Interpretation integrity audit protocol.
- Correction and maintenance: Interpretive debt accumulation and extinction and Interpretive governance maturity model.
Supporting routes
Reading rule
Frameworks organize an intervention. They do not replace the canonical definitions or the evidence chain that makes the intervention defensible.
Navigation
- Framework chains (by usage)
- Pillars (architecture)
- Foundations (canon, authority, response)
- Evidence, audit, observability
- Correction, sustainability, version
- Identity, collisions, identifiers, graphs
- RAG, agentic, closed environments
- Exogenous, multi-AI, maturity
- Multisite and public repositories
Projection rule
The frameworks in this registry are application surfaces. When a concept is defined in /definitions/ and formalized in /doctrine/, doctrine constitutes the canonical source and the framework acts as a structured projection designed to be usable in real context.
In case of perceived discrepancy between a framework and a doctrinal page, the doctrinal page prevails.
Framework chains (by usage)
Diagnose and prove
- Interpretation integrity audit: full end-to-end protocol
- Interpretive persistence audit after deletion, correction, or 404
- Canon vs inference mechanics (traceability and proof of fidelity)
- Interpretive observability: metrics, logs, evidence
- IIP-Scoring™: operational method (bounded public view)
Correct and maintain
- Interpretive correction governance (debt resorption)
- Protocol for exogenous deactivation of residual authority
- Interpretive debt: analytical framework
- Interpretive sustainability: correction budget and LTS governance
- Release discipline and version power for the interpreted web
Govern agentic systems and retrieval
- Interpretive governance for AI agents (open web & closed environments)
- Enforceable response conditions for AI agents
- RAG governance: retrieval and inference control
- Governance of closed environments: interpretive enclave and execution control
Pillars (architecture)
- Q-Layer: governance of response conditions (full framework)
- Authority conflict governance: advanced interpretive arbitration
- CTIC: cross-layer transactional coherence
- Governance of dynamic states: volatile variables and interpretive truth
Foundations (canon, authority, response)
- Canon vs inference mechanics (traceability and proof of fidelity)
- Citations, inference, and distortion: why interpretive fidelity matters more than visibility
- Legitimate non-response protocol (rules and tests)
- Enforceable response conditions for AI agents
Evidence, audit, observability
- Interpretation integrity audit: full end-to-end protocol
- Interpretive persistence audit after deletion, correction, or 404
- Interpretive observability: metrics, logs, evidence
- IIP-Scoring™: operational method (bounded public view)
- CTIC: cross-layer transactional coherence
Correction, sustainability, version
- Interpretive correction governance (debt resorption)
- Protocol for exogenous deactivation of residual authority
- Interpretive debt: accumulation dynamics and extinction (complete operating framework)
- Interpretive sustainability: analytical framework and maintenance conditions
- Release discipline and version power for the interpreted web
Identity, collisions, identifiers, graphs
- Entity collision governance (defensive disambiguation)
- Entity collisions and the interpretive graph: advanced stabilization
- Governance of identifiers: multigraph disambiguation and machine-first anchoring
- Exogenous governance: external graph stabilization (process)
- Protocol for exogenous deactivation of residual authority
RAG, agentic, closed environments
- Interpretive governance for AI agents (open web & closed environments)
- Typology of interpretive drifts in agentic systems
- Agentic risk matrix (open web & closed environments)
- RAG governance: retrieval and inference control
- Governance of closed environments: interpretive enclave and execution control
Multisite and public repositories
- Multisite framework for distributed interpretive authority
- Distributed interpretive authority governance: doctrine
- Governance of identifiers: multigraph disambiguation and machine-first anchoring
Exogenous, multi-AI, maturity
- Exogenous governance: external graph stabilization (process)
- Protocol for exogenous deactivation of residual authority
- Multi-AI stabilization: inter-model coherence
- Interpretive governance maturity model: levels, evidence, requirements
- Instability of AI recommendations and interpretive governance
Associated articles (phenomena)
Frameworks are fed by analyses published in Interpretive phenomena. Reference series:
- Post-semantics: when AI thinks, decides, and overrides the text
- Post-semantics: authority drift as jurisdictional default
- Post-semantics on the open web: why governing output is not enough
Authority and scope
Author:
Gautier Dorval
Scope:
Interpretive governance, agentic (open web and closed environments), semantic stabilization, response conditions, auditability, enforceability, variance reduction, entity disambiguation, interpretive debt, interpretive sustainability.
Primary language:
French (Canada). English versions may exist as equivalents, without modifying canonical meaning.
For contextual framing, see Positioning.
Canonical repo: https://github.com/GautierDorval/interpretive-seo
What a framework is on this site
A framework is not a slogan, a template, or a substitute for evidence. It is an operating model that connects a problem, a scope, a set of inputs, a decision structure, and a set of limits. The framework pages are designed to help readers move from vocabulary to method without confusing method with proof.
For example, a framework about RAG governance can define how retrieval, provenance, source admission, and inference control should be organized. It does not prove that a particular generated answer is legitimate. That still requires proof of fidelity, interpretive evidence, and a clear source hierarchy. A framework about AI agents can describe execution boundaries, but it does not create authority for an agent to act.
This distinction is essential to the site’s architecture. Definitions stabilize meaning. Frameworks organize action. Observations document traces. Service pages describe possible engagements. A framework becomes dangerous when it is treated as a certificate of correctness rather than as a disciplined way of asking better questions.
How to choose the right framework
Start with the failure mode. If the issue is that an AI system invents, smooths, or overextends an answer, begin with response legitimacy, inference control, and non-response frameworks. If the issue is that the right source is retrieved but the answer is still wrong, start with retrieval governance, documentary chain, and proof discipline. If the issue is that a brand or entity is confused with a neighboring entity, start with semantic architecture, entity disambiguation, and collision reduction. If the issue is that an agent can execute an action without enough authority, start with agentic governance and execution control.
The same problem can cross several layers. A brand representation failure may involve entity collision, weak canonical surfaces, uncontrolled third-party sources, stale memory, and poor proof discipline. A useful framework does not collapse those layers into a single cause. It shows which layer must be diagnosed first and which layer must not be inferred from another.
Framework families
The interpretive governance family deals with authority, legitimacy, response conditions, source hierarchy, non-response, and governed negation. These frameworks are useful when the question is not only what the system said, but whether it had the authority to say it.
The evidence and audit family deals with proof of fidelity, interpretation traces, auditability, observation, and the canon-output gap. These frameworks are useful when a team needs to move from impressions to documented differences between the canon and the generated output.
The semantic architecture family deals with entities, collisions, graphs, disambiguation, semantic neighborhoods, and cross-system coherence. These frameworks are useful when the organization is visible but still badly framed, blended with adjacent entities, or interpreted through a contaminated neighborhood.
The retrieval and RAG family deals with source admission, corpus admissibility, chunk authority, retrieval provenance, and documentary chain. These frameworks are useful when a system retrieves content but the governing status of that content remains ambiguous.
The agentic and execution family deals with delegated action, tool-mediated authority, multi-agent chains, execution boundaries, transactional coherence, and agentic response conditions. These frameworks are useful when the output is no longer only an answer, but a possible action.
Limits of framework use
A framework cannot replace a source, an audit, or a live observation. It should not be treated as a guarantee that a system will cite the right page, rank the right result, recommend the right service, or follow the intended instruction. It should be used as a disciplined structure for deciding what must be checked, what must be refused, what must be separated, and what must be documented.
That is also why the frameworks are connected to the SERP ownership map. A framework may discuss several concepts, but it should not steal the primary role of the canonical definition or the service page. When a framework uses a term such as answer legitimacy, semantic architecture, AI visibility audit, or RAG governance, it should support the primary page rather than compete with it.
Practical use
Use frameworks to prepare an audit, structure a diagnosis, brief a stakeholder, or define what evidence must be collected. Do not use them as stand-alone promises. A serious application should always reconnect the framework to definitions, observations, proof artifacts, source hierarchy, and response conditions.
Internal routes to reinforce
These links keep frameworks surfaces visible when they support disambiguation, evidence, service routing, or canonical reading, without making them depend only on template-generated listings.
- Anti-interpretive capture (defense against signal saturation) · Authority conflict governance: advanced interpretive arbitration · Canon vs inference mechanics (traceability and proof of fidelity) · CTIC: cross-layer transactional coherence · Endogenous governance: canonizing the on-site entity (process) · Governance of closed environments: interpretive enclave and execution control · Governance of dynamic states: volatile variables and interpretive truth · IIP-Scoring™: operational method (bounded public view)
- Interpretive debt: accumulation dynamics and extinction (complete operating framework) · Interpretive debt: analytical framework · Interpretive governance maturity model: levels, evidence, requirements · Interpretive sustainability: analytical framework and maintenance conditions · Interpretive sustainability: correction budget and LTS governance · Legitimate non-response protocol (rules and tests) · Q-Layer: governance of response conditions (full framework) · RAG governance vs interpretive governance
- RAG governance: retrieval and inference control · Release discipline and version power for the interpreted web · Statement-level authority retention framework
AI citation readiness checklist route
Use the AI citation readiness checklist when the question is operational: whether a page or corpus is ready to be retrieved, cited and governed before citation tracking begins.
The checklist should be read with AI-ready structure, preview control, machine-first routing and citation fidelity.
In this section
Matrix for qualifying AI perception stability across identity, category, perimeter, evidence, temporality, recommendability, and cross-system convergence.
Protocol for measuring LLM perception drift from a baseline, a canon, multi-model outputs, and a documented canon-output gap.
A scoring matrix for separating AI citation access, retrieval, extraction, role, fidelity and stability.
Matrix for qualifying whether an AI citation is governing, supporting, illustrative, ornamental, outdated, contradictory or insufficient.
Operational checklist for reviewing whether a page, source or corpus is ready to be retrieved, cited and governed in AI-mediated answers.
A practical framework for mapping the adjacent questions that influence retrieval and AI source selection.
Phantom URL audit provides a method to qualify non-existent but plausible URLs, cluster them, and decide whether to create, redirect, clarify, or keep a 404.
Framework for auditing a site’s ability to be understood, traversed, and acted upon by AI agents through visual, HTML, and accessibility signals.
Agentic risk matrix (open web & closed… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
CTIC defines the minimum coherence required across interpretive, governance, and execution layers when a state change or transactional effect is involved.
Interpretive governance for AI agents (open… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Enforceable response conditions for AI agents presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Framework for governing closed environments where AI systems do not only answer but trigger or influence execution inside bounded business systems.
A diagnostic framework for testing whether an extracted statement retains issuer, source, time, scope, status, and interpretive limits inside an AI response.
Interpretive persistence audit after… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Protocol for exogenous deactivation of… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Multisite framework for distributed… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Entity collisions and the interpretive… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Interpretation integrity audit: full… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Framework for building an observability layer around interpretive stability, using metrics, logs, and evidence without confusing observation with attestation.
Machine-first visibility operating model presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Interpretive debt: accumulation dynamics and… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Typology of interpretive drifts in agentic… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Citations, inference, and distortion: why… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Endogenous governance: canonizing the… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Exogenous governance: external graph… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Governance of identifiers: multigraph… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Framework for handling conflicting authorities without collapsing them into a false consensus or an arbitrary narrative shortcut.
Framework for resisting interpretive capture when repeated, proximate, or dominant signals saturate a system and begin to replace the canon.
Framework for distinguishing canon from inference and for producing proof of fidelity that keeps high-impact outputs inside declared canonical bounds.
Framework for handling volatile states, changing variables, and time-bounded truths without turning temporary data into stable doctrine.
Framework for handling entity collisions and preventing one entity from absorbing the properties, evidence, or authority of another.
Public bounded method for running IIP-Scoring™ without disclosing private thresholds or internal calibration logic.
Framework for understanding why AI recommendations drift across contexts and how interpretive governance can bound recommendation instability.
Framework for correcting interpretive drift over time by identifying debt, prioritizing remediation, and preventing recurrence after publication.
Analytical framework for identifying, classifying, and monitoring interpretive debt as the accumulation of unresolved distortions in an interpreted web.
Interpretive governance maturity model… presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Analytical framework for evaluating whether an interpretive governance regime can remain stable, maintainable, and governable over time.
Framework for allocating correction budget and establishing long-term support discipline for interpretive governance surfaces.
Protocol defining when an AI system should abstain, request clarification, or explicitly refuse to conclude because the canon does not authorize the answer.
Framework for reducing interpretive variance across several AI systems by stabilizing the canonical surface rather than optimizing isolated prompts.
Full framework for governing the conditions under which a response may be produced, qualified, narrowed, or refused.
RAG governance: retrieval and inference control presents an operational framework for governing interpretation, authority, evidence and AI response conditions.
Framework explaining why RAG governance is not equivalent to interpretive governance and why the latter remains broader than retrieval architecture.
Framework for treating interpretive correction like software maintenance: release discipline, change visibility, rollback logic, and continuity over time.
Strategic external references
These references extend the doctrine, the test suite, the manifest, and the related public corpora.
External doctrine and reference site.
Main doctrine, implementation repository and orientation principles.
Simulation reference for authority governance.
Test suite for expected governance behaviors.
SSA-E + A2 doctrine and dual web corpus.
Agentic reference and closed-environment corpus.