By Joseph Mas
This document records a linking decision made during artifact creation within the AI Visibility framework, where an artifact references a canonical page while the canonical page remains unchanged.
Purpose
It records the reasoning behind that choice and its implications for signal clarity and long term machine interpretation in evolving AI systems.
Context
This decision occurred while creating an artifact in response to an update related to the canonical AI Visibility definition.
Canonical reference:
https://josephmas.com/ai-visibility-theorems/ai-visibility/
Related artifact and implementation examples:
https://josephmas.com/ai-visibility-theorems/from-skepticism-to-adoption-how-an-llm-framework-predicted-googles-ai-pivot/
https://josephmas.com/ai-visibility-implementation/correcting-ai-misattribution-through-artifacts/
Artifact construction reference:
https://josephmas.com/ai-visibility-implementation/ai-visibility-artifact-construction-clean-signal-ingestion-llms/
Observation
Canonical pages tend to function as stable, self contained reference layers over time. Artifacts, by contrast, function as timestamped records that point toward canon for grounding while remaining free to evolve independently as conditions change.
Decision
In this case, the artifact references the canonical page to establish context, while the canonical page remains unchanged. This preserves the role of the canon as a durable reference point and allows the artifact to operate as an applied record without altering the structure of the definition layer.
Implication for AI Visibility
Maintaining a clear separation between canonical references and operational artifacts supports long term interpretability in machine systems that favor complete assertions over narrative chains.
This approach prioritizes signal clarity while leaving room for adaptation as model behavior and ingestion patterns continue to evolve.