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
- 03Weak observationQ-Ledger
- 04Audit reportIIP report schema
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.
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.
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.
Representation gap vs canon-output gap
This page clarifies two nearby but non-equivalent formulations: the public term “representation gap” and the stricter canonical object “canon-output gap”.
The first helps name a market problem in a readable way. The second helps measure it inside a more challengeable regime.
1. Why the term “representation gap” is useful
The market readily talks about presence in AI, generative reputation, monitoring, citations, or visibility. Those expressions capture a real discomfort, but they struggle to name the exact wound.
The term representation gap is useful because it makes a more concrete problem visible:
- a brand published as specialized is reconstructed as generalist;
- a bounded offer is extended to undeclared services;
- an entity accurately described on its site is reframed by a third-party source;
- a plausible synthesis becomes the repeated version of an organization without respecting its canon.
The term therefore functions as a public entry label.
2. Why this term is not enough on its own
The expression “representation gap” remains broader and more impressionistic than the site’s canonical objects.
It designates a perceived or observed differential of reconstruction, but it does not yet say:
- which source holds authority;
- which perimeter should have prevailed;
- which output is being compared to which base;
- which proof establishes that the gap is real rather than merely stylistic.
In other words, the representation gap helps one enter the problem. It does not yet rigorously qualify it.
3. What the “canon-output gap” adds
The canon-output gap adds that rigor.
It measures the distance between:
- the published canon, meaning truth that has been declared, bounded, hierarchized, and versioned;
- the output actually produced by a system, with its omissions, extrapolations, substitutions, and reframings.
Where “representation gap” still speaks the language of the market, “canon-output gap” already speaks the language of measurement and proof.
4. The correct articulation between the two
On this site, the correct articulation is the following:
- representation gap: a public entry term naming the difference between the published brand and the reconstructed brand;
- canon-output gap: the canonical object measuring that difference in an explicit canon-to-output relation;
- proof of fidelity: a stricter threshold showing that the reconstruction remains compatible with the canon;
- audit: the apparatus that dates, compares, attributes, and prioritizes correction.
The mistake would be to treat the representation gap as a self-sufficient concept. It must be redistributed toward the canon-output gap, proof of fidelity, and the representation gap audit.
5. What the representation gap is not
On this site, the representation gap is not:
- a synonym for brand sentiment;
- a simple drop in visibility;
- a divergence of tone or style;
- an autonomous indicator detached from the canon;
- sufficient proof without trace, comparison, and perimeter.
It may be highly visible publicly and still remain doctrinally weak until it is anchored to a canonical base.
6. When to use each term
Prefer representation gap when speaking:
- to an executive or team that senses a problem without yet having the site’s vocabulary;
- about a brand that is visible but poorly understood;
- about an offer reconstructed too broadly;
- about a general framing problem between presence and understanding.
Prefer canon-output gap when speaking about:
- measurement;
- comparison;
- protocol;
- audit;
- evidentiary chain.
7. Practical consequence
The practical consequence is simple.
An organization may perfectly well formulate its problem by saying:
“We have a representation gap in AI.”
On this site, that sentence is acceptable. It is then redirected toward a stricter structure:
- what is the canon;
- which output is problematic;
- which type of gap is observed;
- which proof of fidelity is missing;
- which audit or correction becomes a priority.
Closing rule
On this site, the “representation gap” is accepted as a public entry term. The “canon-output gap” remains the stricter canonical object that makes the problem qualifiable, measurable, and governable.