Research
I study how meaning is preserved, transformed, or lost when language, narrative, law, and cultural expression pass through artificial intelligence and other computational systems.
Central question
What changes when a machine translates a sentence, summarizes a story, predicts a legal outcome, or learns from cultural expression?
The work combines technical analysis with translation studies, narratology, legal reasoning, and digital humanities. NFAF serves as a developing methodology for examining voice, cadence, imagery, cultural register, semantic drift, and narrative structure.
Methodology
The Narrative Flattening Analysis Framework, an original framework developed by Johennie Helton, is a modular method for identifying and describing changes in narrative signal across AI-mediated transformations.
Read the specification → About NFAF →Current program
Research into the ways AI systems transform language and narrative, especially across translation, rewriting, summarization, and other forms of machine-mediated expression. The focus is not only whether information survives, but whether voice, intensity, ambiguity, cultural context, and emotional texture survive with it.
Explore AI mediationLiving notebook
A whiteboard of fragments, questions, quotations, memories, and observations. This is where unfinished thoughts remain visible long enough to become something else.
Open the whiteboardFoundations
Earlier work in classifiers, legal-text analysis, copyright case prediction, and bilingual legal translation established the technical and methodological foundation for the current research.
View technical foundationsResearch history
A completed 2024 manuscript became the point of departure for the research into translation, emotional register, imagery, memory, and the preservation of narrative meaning.
Read about the originsSelected bibliography
A curated set of books, papers, and technical resources from translation studies, narratology, digital humanities, machine learning, law, and algorithmic culture.
Visit the Reading NookResearch pathway
Classification experiments and a copyright-case prediction capstone established the groundwork in model evaluation, legal-text processing, and domain-specific analysis.
Round-trip translation studies examined lexical drift in English–Spanish legal text, followed by exploratory work on emotional register, imagery, rhythm, and narrative voice.
The research now brings these strands together through NFAF and a broader inquiry into how computational systems alter the signals through which meaning is carried.
Research links