In high-efficiency automated infrastructures, human meaning is not merely lost or degraded; it is systematically re-encoded. Context Collapse, in this setting, is not a transient interpretive error but a persistent environmental constraint. This working paper offers a canonical formulation of Context Collapse as a structural field that forcibly compresses multidimensional human context into machine-legible signals. By mapping the dimensions of this collapse and identifying canonical compression regimes, the paper specifies the conditions under which Human Drift becomes necessary for meaning-bearing intent to remain operative. The framework extends the IDM Spiral Model and the canonical account of Human Drift by moving beyond sociological “audience collision” toward a structural analysis of semantic erasure and re-encoding in automated systems.
Zeev Singer (Sat,) studied this question.