In the previous stage of this research, I began with a simple distinction: Information is not the same as meaning. A signal may arrive perfectly and still fail to matter. Words may be heard without being understood. An image may be seen without being absorbed. Information can pass through a system without becoming part of it. This led to a new question: What determines whether information becomes meaning? I use η(t) as a working symbol for this conversion efficiency. In its s

“Resonance Efficiency η(t)” This log introduces a distinction that becomes necessary once communication is scaled. Shannon’s information theory defines how symbols are transmitted. It measures uncertainty and optimizes signal transfer under noise. In that framework, meaning is deliberately excluded. A message can be perfectly transmitted without being meaningfully received. This separation becomes visible when communication increases, but coherence does not. To address this,