Claude Shannon's 1948 entropy formula H = p log p established the
mathematical foundation of information theory, quantifying uncertainty in
discrete symbol distributions within communication channels. This paper
introduces...
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Claude Shannon's 1948 entropy formula H = p log p established the
mathematical foundation of information theory, quantifying uncertainty in
discrete symbol distributions within communication channels. This paper
introduces Narrative Entropy (S), a formally distinct but epistemologically
related metric developed within the Bulut Doctrine framework, which measures
the dynamic accumulation of cognitive resistance and structural uncertainty
across the temporal dimension of narrative experience. We demonstrate that
while S draws its foundational intuition from Shannon's H, the two metrics differ
across six critical dimensions: domain, temporal structure, measurement object,
operational unit, directionality, and engineering function. S is not an application
of Shannon entropy to literature it is a structurally independent metric that
addresses phenomena Shannon's framework was not designed to capture. We
further
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