🤖 AI Summary
This study addresses the challenge of quantifying creative transformation in literary works—not merely isolated novelty—by proposing the first multi-level textual representation framework that integrates lexical, semantic, conceptual, structural, and narrative dimensions. Grounded in Tarde’s and Baldwin’s theories of imitation, the approach combines directional alignment with calibrated similarity metrics to systematically analyze inheritance and variation across these levels. The method effectively uncovers patterns of retention and divergence between texts, precisely identifying points of sustained imitation and creative departure. Validation against historically documented literary relationships demonstrates the model’s capacity to capture nuanced intertextual dynamics, offering a rigorous computational lens for studying literary influence and innovation.
📝 Abstract
Creativity is often framed as the production of novelty, yet many cultural works emerge through transformation of earlier artifacts and not through isolated invention. Drawing on theories of imitation by Gabriel Tarde and James Mark Baldwin, this paper models creativity as selective transformation across multiple levels of textual representation. We introduce a multi-level framework that compares literary texts across lexical, semantic, conceptual, structural, and narrative dimensions using directional alignment and control calibrated similarity measures. Applying the model to historically documented literary relationships, we show that different pairs preserve source structure at different representational levels while diverging in others. These transformation profiles provide a quantitative method for characterizing how imitation persists and where creative divergence occurs within literary works.