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Interpretive governance, semantic architecture, and machine readability.

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Glossary

Glossary: capture, contamination, collisions

Interpretive capture, neighborhood contamination, interpretive collision, and invisibilization: understanding the signal warfare that distorts an entity truth in AI responses, and linking these phenomena to canonical definitions and frameworks.

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CollectionGlossary
TypeGlossary
Domaincapture-contamination-collisions
Published2026-02-20
Updated2026-03-11

Glossary: capture, contamination, collisions

This family groups the exogenous phenomena that distort the interpretation of an entity by AI systems (LLMs, generative engines, agents, RAG). Here, the problem is not merely the internal quality of a site or corpus: it is the external dynamics of signals, co-occurrences, entity confusions, and semantic dominance effects.

Each entry links to: a canonical definition, a framework (if applicable), and related pages (doctrine, clarifications, stabilization frameworks).

Quick access

Terms in the “capture, contamination, collisions” family

Interpretive capture

Situation where an actor, a set of sources, or a semantic neighborhood imposes a dominant framing, to the point of diverting the interpretation of an entity or concept in AI responses.

Neighborhood contamination

Pollution of an entity by its dominant co-occurrences: AI ends up defining the entity by what surrounds it, rather than by its canon.

Interpretive collision

Fusion or confusion between two distinct entities when their signals are sufficiently close to be mixed by AI (synthesis hallucination, cross-attribution, feature blending).

Interpretive invisibilization

Information exists (indexed, accessible), but does not exist in the generated response: it is discarded by the model, by the retrieval, by the framing, or by a competing authority.

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