Semantic Sensing

Semantic Sensing is the methodology used to detect emergent meaning before it stabilizes into recognizable signals, categories, or trends. Within the IDM Spiral Model, Semantic Sensing is the sensing layer that detects pre-baseline drift under Context Collapse.

It operates under conditions of Context Collapse, where situational depth erodes faster than meaning can consolidate. Unlike traditional analytics, Semantic Sensing identifies meaning formation before it solidifies into recognizable categories, norms, or trends - tracking how it mutates through Human Drift before stabilization into baseline.

The methodology is activated and interpreted through Mode H09, which prioritizes deep contextual fidelity over surface-level signals. It provides the necessary resolution to distinguish between random noise and directional drift.

Related: Context Collapse | Human Drift | Human Intent | Mode H09 | IDM Spiral Model Architecture | Codex Index