NFAF: Narrative Flattening Analysis Framework

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.

Note: NFAF is published as pre-existing intellectual property of Johennie Helton. This specification is provided for research and citation purposes only. No rights to implement, commercialize, or assume co-ownership of the framework are granted or implied.

• Narrative In this framework, narrative is defined broadly as any structured account: linguistic, visual, or multimodal, that conveys meaning through sequence, framing, or voice. Narrative encompasses not only literary texts but also journalistic reporting, scientific writing, legal argument, and cultural expression. Narratives may be linear or nonlinear, explicit or implicit, but they are always shaped by choices of form, style, and emphasis that reflect particular contexts and perspectives.

• Flattening: is the systematic reduction of distinctiveness that occurs when a narrative is mediated by AI. Flattening can manifest in two directions:
  Erosion: the loss or weakening of semantic nuance, stylistic rhythm, cultural reference, or affective tone.
  Inflation: the insertion or exaggeration of generic features not present in the original. Though opposite in form, both processes converge on the same outcome: outputs that are more standardized, less contextually situated, and less distinctive.

• Narrative flattening: refers to the erosion or inflation of signals that constitute narrative distinctiveness, including voice, style, cultural reference, and domain-specific markers of meaning. In literary contexts, this may appear as homogenized rhythm, reduced figurative density, or neutralized emotional tone. In other domains, it may appear as genericized news summaries, culturally inaccurate image generation, biomedical writing that converges on formulaic phrasing, or legally imprecise translations. Narrative flattening thus captures the tendency of AI-mediated processes to prioritize fluency, generality, and efficiency over depth, specificity, and contextual nuance.

• Semantic drift: Systematic or cumulative change in meaning between source and transformed text.

• Alignment: Segment-level mapping between source and transformed narratives to enable side-by-side analysis.

• Signal preservation: Degree to which tone, imagery, prosody, and culturally loaded forms are retained.