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Clarification

AI Citation Registry vs interpretive governance

Clarification distinguishing AI citation registries as provenance infrastructure from interpretive governance as a broader authority, perimeter, and response-legitimacy layer.

CollectionClarification
TypeClarification
Version1.0
Stabilization2026-04-28
Published2026-04-28
Updated2026-04-28

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
    Response authorizationQ-Layer: response legitimacy
  3. 03
    External contextCitations
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.
Legitimacy layer#02

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.
Citation surface#03

Citations

/citations.md

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

Makes provable
That an external reference can be cited as explicit context rather than silently inferred.
Does not prove
Neither endorsement, neutrality, nor the fidelity of a final answer.
Use when
When a page uses external sources, sector references, or vocabulary anchors.

AI Citation Registry vs interpretive governance

An AI Citation Registry can preserve attribution. Interpretive governance is broader: it determines whether a cited, extracted, or reused statement remains legitimate inside the response.

This distinction prevents a useful provenance idea from being mistaken for the whole governance layer.

What an AI Citation Registry can solve

A citation registry can help a system identify:

  • the issuing source;
  • the canonical location of a statement;
  • the publication or update time;
  • the jurisdiction or issuing body;
  • whether a fragment should be cited as official, archival, superseded, or contextual.

That is valuable. It improves the portability of citation and reduces weak attribution.

What it does not fully solve

Citation infrastructure does not automatically decide:

  • whether the answer preserved the source’s perimeter;
  • whether the cited statement was generalized beyond scope;
  • whether a derivative source structured the response more strongly than the official source;
  • whether the system should have asked for clarification;
  • whether non-response would have been more legitimate than completion.

Those questions belong to interpretive governance, authority boundary, proof of fidelity, and Q-Layer arbitration.

Site position

A citation registry is a possible implementation of authority preservation at the provenance layer.

Interpretive governance is the broader doctrine that asks what the response may do with the cited material once provenance has been established.

Minimal rule

A cited source is not automatically a governing source. A registry can help establish where a statement comes from; it does not by itself prove that the final answer preserved meaning, perimeter, modality, and limits.