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.
- 01Evidence artifactfalse-neighbors.json
- 02Evidence artifactsemantic-proximity-separation.json
- 03Evidence artifactcommon-misinterpretations.json
false-neighbors.json
/false-neighbors.json
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
semantic-proximity-separation.json
/semantic-proximity-separation.json
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
common-misinterpretations.json
/common-misinterpretations.json
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
Brand consistency vs AI perception stability
Brand consistency concerns what the organization controls: names, messages, tone, visual identity, offer, evidence and behaviour. AI perception stability concerns what external systems reconstruct across prompts, models, languages, regions and dates.
Strong consistency reduces ambiguity but does not guarantee external stability. Historical sources, third parties, retrieval and model variability may still shift the portrait.
Identical answers or stable invariants?
Stability does not mean every answer should be identical. Different wording may preserve identity, category, scope and attribution. Conversely, identical answers may repeat a stable error.
| Test | Brand consistency | Perception stability |
|---|---|---|
| Alignment of controlled messages | Central | Input only |
| Variation across models | Out of scope | Central |
| Preservation of boundaries | Desired | Observed |
| Repetition of an error | May reduce it | May reveal stable drift |
| Dated baseline | Useful | Essential |
Measurement rule
Assess material invariants: identity, category, scope, time, relations, authority and recommendability. Do not penalize stylistic variety that preserves them.
Consistency prepares the corpus. Stability describes observed behaviour. Governance connects them without promising equivalence.