This is the first public release of the Narrative Flattening Analysis Framework (NFAF). The Narrative Flattening Analysis Framework (NFAF) is a methodology for detecting and analyzing the loss or convergence of distinct cultural, emotional, and stylistic features in narratives as they pass through automated processes such as machine translation, summarization, rewriting, or other AI-mediated transformations. Narrative is defined here in its broadest sense. NFAF is adaptable to a wide range of use cases and modalities. It outlines modular stages for alignment, semantic drift detection, and qualitative–quantitative analysis, enabling reviewers to identify meaning loss or homogenization including subtle or accumulative cultural, political, or semantic transformation. Designed to balance rigor with flexibility, NFAF provides a replicable structure for future research while remaining open to domain-specific refinement. Current focus: Applying NFAF to narrative voice in literary text, with emphasis on preserving cultural and emotional signals in AI-mediated transformation. Intellectual Property Notice This framework is provided for research and citation purposes only. No rights to implement, commercialize, or assume co-ownership of the framework are granted or implied. For permissions beyond research use, please contact the author. Repository: https://github.com/johennie/nfaf-public

If you use NFAF in research, please cite this work.