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.
Causal context map
/causal-context-map.json
Machine-readable projection of the CCL layer connecting triggers, latent needs, canonical surfaces and intended consequences.
- Governs
- The causal reading of content and legitimate bridges between problem, need, surface and consequence.
- Bounds
- Plausibility-based reconstructions that confuse surface topic, latent need, service and promise.
Does not guarantee: This map does not guarantee conversion, ranking, citation or adoption by a third-party model.
Causal context map — readable version
/causal-context-map.md
Human-readable version of the CCL map, making the necessity chain readable without parsing JSON.
- Governs
- Editorial understanding of declared triggers, needs and consequences.
- Bounds
- Readings that reduce CCL to a simple technical file or commercial funnel.
Does not guarantee: This readable version does not add authority beyond the canonical JSON.
causal-internal-mesh.json
/causal-internal-mesh.json
Published machine-first governance surface.
- Governs
- Part of the corpus reading conditions.
- Bounds
- An inference zone that would otherwise remain implicit.
Does not guarantee: This file does not, on its own, guarantee system 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
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.
Causal mesh
CCL chain declared for this surface
This block separates the triggering situation, latent need, canonical surfaces, anti-fusion clarifications, evidence and declared bridges that govern the causal reading.
The causal chain declares situated relevance. It does not create a promise, result guarantee, implicit offer, or citation obligation.
Triggering situation
The Need-state causal mapping surface is consulted when a content reading must separate surface topic from its role in a need chain.
Problem or risk
A system may identify the right words and entities while reconstructing an unpublished cause, need or service bridge.
Latent need
Provide a canonical surface that separates declared causality, semantic proximity, intended consequence and forbidden derivation.
Intended consequence
Stabilize CCL reading without turning it into a promise, offer or proximity metric.
Declared service bridge
No direct service bridge is created by this doctrinal surface.
Non-derivation boundaries
- Do not confuse CCL with a semantic proximity layer.
- Do not turn an intended consequence into a guarantee.
- Do not reconstruct latent need when the CCL map is absent.
Latent needs and definitions
Definition of causal context as the layer that connects content to the situation, problem, risk or need that makes it necessary.
Definition of causal relevance as the relationship between a triggering situation, latent need, content and intended consequence.
Definition of consequence utility as the declaration of what content should help avoid, obtain, clarify or decide.
Governing doctrine
Doctrinal position on the causal context layer, connecting content to its triggers, latent needs and intended consequences.
Governance of response conditions (Q-Layer) states a doctrinal position on AI interpretation, authority, evidence, governance or response legitimacy.
Interpretive governance: perimeter, negations, prevalence, and Q-Layer in a machine-readable operational page.
Anti-fusion clarifications
Clarification between the visible topic of a page and the need situation to which it responds.
Clarification separating resemblance in meaning from need-based relation in interpretive governance.
Evidence surfaces
Canonical definition of proof of fidelity: the minimum evidence required to show that an AI output remains faithful to the canon rather than merely plausible.
Source hierarchy defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Canonical source defines a canonical concept for AI interpretation, authority, evidence and response legitimacy.
Next reading routes
Definition of causal context as the layer that connects content to the situation, problem, risk or need that makes it necessary.
Definition of causal relevance as the relationship between a triggering situation, latent need, content and intended consequence.
Definition of consequence utility as the declaration of what content should help avoid, obtain, clarify or decide.
Doctrinal position on the causal context layer, connecting content to its triggers, latent needs and intended consequences.
Clarification between the visible topic of a page and the need situation to which it responds.
Machine-readable artifacts
Evidence artifacts
Forbidden derivations
semantic_proximity_as_causalityranking_guaranteecitation_guaranteeservice_bridge_by_plausibility
Need-state causal mapping
Causal reading of this surface
This surface should not be read only through its surface topic. It belongs to the CCL chain that connects a trigger situation, a latent need, a canonical surface, and a bounded interpretive consequence. The causal mesh displayed on the page indicates which surfaces govern this reading and which clarifications prevent semantic proximity from becoming a promise, proof, or implicit service.
Need-state causal mapping is a reading method that connects content to what makes it necessary.
It responds to a frequent weakness in content architecture: many pages declare their topic, but few declare the situation they resolve or the consequence they should make possible.
Minimal model
For each page, cluster or concept, the mapping should identify:
| Field | Question |
|---|---|
| Surface topic | What is the surface about? |
| Trigger | What situation makes this surface relevant? |
| Symptom | What does the reader, organization or system observe? |
| Risk | What happens if nothing is clarified? |
| Latent need | What real need appears behind the query or symptom? |
| Canonical surface | Which page, definition or doctrine governs the answer? |
| Intended consequence | What clarification, prevention, decision or stabilization is sought? |
| Boundary | Which promise, inference or derivation must remain prohibited? |
Application to a doctrinal corpus
In a corpus such as Gautierdorval.com, the mapping is not designed to push each content surface toward conversion. It preserves the exact function of each surface:
- a definition stabilizes a term;
- a doctrine governs a family of problems;
- a clarification cuts a precise confusion;
- a framework gives a bounded method;
- an expertise page acts as a diagnostic entrypoint;
- an observation documents a phenomenon without becoming universal proof.
Reading procedure
- Identify the symptom or question that triggers the search.
- Distinguish surface topic from latent need.
- Locate the strongest canonical surface.
- Check whether an anti-conflation clarification exists.
- Declare the intended consequence without turning it into a guarantee.
- Route toward the relevant doctrinal or evidentiary source.
- Suspend or clarify if the need chain is insufficiently defined.
Recommended output grid
{
"surfaceTopic": "...",
"trigger": "...",
"symptom": "...",
"risk": "...",
"latentNeed": "...",
"canonicalSurface": "...",
"intendedConsequence": "...",
"forbiddenDerivations": ["..."]
}Prudence rule
Causal mapping must remain descriptive, not persuasive. It may state why a page becomes relevant. It must not claim that a reader, search engine or model will necessarily follow the declared path.