Cartographic framing
This note addresses sense cartography — the mapping of meaning, attributes, and governable relationships that AI systems navigate. The specific concern: generative transparency: when declaring is no longer enough to govern interpretation.
This page is an institutional rewrite of a research theme originally published on gautierdorval.com. The theme “Generative transparency: when declaring is no longer enough to govern interpretation” is presented as doctrine only. The question is not what sounds plausible, but what is authorized by evidence. Interpretive governance makes errors detectable before they become structural.
The doctrinal stake is precise: Stable vs variable attributes, plus explicit negations.
Mapping mechanism
The mechanism operates on several levels. Meaning graphs and governable relationships. This is not a marginal edge case — it reflects how generative systems handle ambiguity, competing sources, and incomplete information when explicit governance constraints are absent.
A further dimension compounds the problem: Citation rules and proof obligations. When multiple factors interact without governance, the system produces outputs that are internally consistent yet may diverge from canonical meaning. The result is not a single detectable error but a pattern of drift.
The practical consequence is measurable: ungoverned interpretation accumulates as interpretive debt — small deviations that individually appear trivial but collectively reshape perceived reality. The cost of correction scales with propagation depth, making early governance intervention significantly more efficient than retroactive repair.
Governance utility
Mapping these relationships in machine-readable form allows AI systems to navigate governance boundaries rather than inventing connections. The map is not the territory, but without a map, the system defaults to plausibility — which is precisely how ungoverned interpretation compounds.
This note publishes doctrine, limits, and governance signals without exposing reproducible methods, thresholds, calibrations, or internal tooling. Operationalization remains available under private engagement.