New framework models literary creativity through textual transformation

This paper introduces a novel framework for modeling creativity in literary texts by analyzing transformations across multiple representational levels, including lexical, semantic, conceptual, structural, and narrative dimensions. The authors draw upon theories of imitation from Gabriel Tarde and James Mark Baldwin to develop directional alignment and calibrated similarity measures. By applying this model to documented literary relationships, the research quantifies how imitation and creative divergence occur, offering a new method for characterizing these processes. AI

IMPACT This research offers a computational approach to understanding creativity and imitation in literature, potentially influencing AI models designed for creative writing or literary analysis.

RANK_REASON The item is an academic paper published on arXiv detailing a new computational model for analyzing literary texts. [lever_c_demoted from research: ic=1 ai=0.4]

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