Gautier Dorval
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Interpretive governance, semantic architecture, and machine readability.

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Expertise

AI citation analysis

Service-facing expertise entry for AI citation analysis: reading citations, mobilized sources, and framing losses without confusing reference presence with faithful understanding.

CollectionExpertise
TypeExpertise
Domainai-citation-analysis

Engagement decision

How to recognize that this axis should be mobilized

Use this page as a decision page. The objective is not only to understand the concept, but to identify the symptoms, framing errors, use cases, and surfaces to open in order to correct the right problem.

Typical symptoms

  • The team sees citations or references to the brand in AI answers, but cannot tell whether the framing remained faithful.
  • The official site is cited, but a third party seems to impose the category, comparison, or limit that actually prevails.
  • A source is repeatedly mentioned without its exclusions, conditions, or negations being preserved.
  • Citation dashboards exist, but no protocol distinguishes presence, understanding, fidelity, and stability.

Frequent framing errors

  • Treating citation volume as proof of understanding.
  • Assuming that a cited source is necessarily the governing source of the answer.
  • Ignoring uncited sources that nevertheless frame the synthesis.
  • Comparing citations without a canon, source hierarchy, and critical attributes.

Use cases

  • Reading a corpus of citations to determine whether the problem belongs to visibility, framing, or fidelity.
  • Qualifying the difference between a cited source, a structuring source, and a governing source.
  • Deciding when to move from citation reading to proof of fidelity, a comparative audit, or a representation gap audit.
  • Detecting recurring omissions of perimeter, modality, or negation in apparently well-sourced answers.

What gets corrected concretely

  • Explicit separation between citation, structural mobilization, faithful understanding, and stability.
  • Linking each citation to the exact object, perimeter, and modality it was supposed to preserve.
  • Mapping cited sources, framing sources, and silently governing sources.
  • Turning citation tracking into an audit and correction protocol.

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. 01Definitions canon
  2. 02Observatory map
  3. 03Q-Ledger JSON
Canon and identity#01

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.

Observability#02

Observatory map

/observations/observatory-map.json

Structured map of observation surfaces and monitored zones.

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.

Observability#03

Q-Ledger JSON

/.well-known/q-ledger.json

Machine-first journal of observations, baselines, and versioned gaps.

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 (2)

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

Observability#04

Q-Metrics JSON

/.well-known/q-metrics.json

Descriptive metrics surface for observing gaps, snapshots, and comparisons.

Policy and legitimacy#05

Citations

/citations.md

Surface that makes explicit the conditions of response, restraint, escalation, or non-response.

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
    Observation mapObservatory map
  3. 03
    Weak observationQ-Ledger
  4. 04
    Derived measurementQ-Metrics
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 index#02

Observatory map

/observations/observatory-map.json

Machine-first index of published observation resources, snapshots, and comparison points.

Makes provable
Where the observation objects used in an evidence chain are located.
Does not prove
Neither the quality of a result nor the fidelity of a particular response.
Use when
To locate baselines, ledgers, snapshots, and derived artifacts.
Observation ledger#03

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#04

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.
Complementary probative surfaces (2)

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

Report schemaAudit report

IIP report schema

/iip-report.schema.json

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

Citation surfaceExternal context

Citations

/citations.md

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

AI citation analysis

This page captures a service-facing label. On this site, “AI citation analysis” designates a governed reading of citations, references, and patterns of documentary mobilization inside generative outputs.

The objective is not to count citations for their own sake. The objective is to know what they show, what they hide, and from what point they stop being a mere presence signal.

What this label names on this site

AI citation analysis first serves to answer questions such as these:

  • which sources are cited, and in which answer types;
  • which source carries the apparent object of the answer;
  • which source actually imposes the framing, comparison, or category;
  • which limits disappear even when the official source is displayed;
  • which citations recur stably, but with reconstructed meaning that still shifts.

Taken this way, the work is useful. It turns a pile of screenshots into a more explainable reading.

What this layer can legitimately do

A serious layer of AI citation analysis can legitimately:

  • distinguish the cited source, the structuring source, and the governing source;
  • identify cases where the citation preserves the object but loses the perimeter;
  • show that an official source is visible without remaining normative;
  • reveal which third-party surfaces silently orient the synthesis;
  • prepare the move toward a stricter proof regime.

In other words, this layer helps read the real function of citation inside the answer.

Where this layer stops

Citation analysis stops as soon as a stronger conclusion would be required:

  • that the answer remained inside the canon;
  • that exclusions, negations, and limits are preserved;
  • that a cited source is indeed the authority source that prevailed;
  • that a local citation is enough to demonstrate cross-system stability;
  • that citation volume counts as proof of understanding.

At that level, one must move upward toward Being cited vs being understood, proof of fidelity, the representation gap, and often the representation gap audit.

The reading rule used here

On this site, the rule is simple:

  • use AI citation analysis when the dominant layer consists in reading citation logs, screenshots, or sourced outputs;
  • use being cited vs being understood when the public distinction must be made explicit;
  • use structural visibility when a source acts without being displayed;
  • use AI source mapping when the real distribution of roles between visible, structuring, and governing sources must be qualified;
  • use proof of fidelity when a stronger claim about fidelity becomes necessary;
  • use audit when the question is no longer merely to read citations, but to explain, attribute, and correct.

When this entry point becomes useful

This entry point becomes especially useful when:

  • citations multiply, but confidence in real understanding remains low;
  • the official site is cited, but the reconstructed perimeter still drifts;
  • a team wants to know which third parties frame the answer even when they are not always visible;
  • an AI Search Monitoring setup already exists, but no longer suffices to read the real quality of citations.

What this label does not replace

AI citation analysis does not replace:

It constitutes a useful intermediate layer: more interpretive than simple monitoring, but less probative than a full audit.

Doctrinal map

On this site, “AI citation analysis” redistributes toward:

Back to the map: Expertise.

Related content

Being cited vs being understood

Clarification distinguishing citation as a signal of documentary presence and understanding as faithful preservation of object, perimeter, modality, and limits.

Clarification
Being cited is not being understood

A source may be cited by AI and still lose its limits, authority, or framing. The real diagnosis starts not at the citation itself, but at what the citation preserves or abandons.

Article architecture semantique 6 min
AI source mapping

Service-facing expertise entry for mapping source roles in AI answers: cited source, structuring source, governing source, and the hierarchy that actually prevails.

Expertise
Cited source vs structuring source vs governing source

Clarification distinguishing the source displayed in the answer, the source that changes the shape of the possible answer, and the source whose authority actually prevails in the final reconstruction.

Clarification
When the cited source is not the governing source

An official source may appear inside an AI answer while still losing the framing, comparison, or limits that actually govern the final synthesis.

Article architecture semantique 8 min
AI Search Monitoring

Service-facing expertise entry for AI Search Monitoring: tracking citations, appearances, and visible gaps without confusing descriptive observation with representation governance.

Expertise
AI Search Monitoring vs representation governance

Clarification distinguishing AI Search Monitoring as a descriptive monitoring layer and representation governance as the work of bounding, proving, and correcting reconstructed meaning.

Clarification
Representation gap

Public hub that reframes 'visibility in AI' as a gap between the published brand and the brand reconstructed by AI systems.

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