Reception
What happened after Katherine Elkins’s work entered the world: the methods later researchers adopted, the frameworks they extended, and the fields into which the work traveled.
Original contributions are stated separately from their subsequent use. Joint research is attributed to its full authorship; independently led work remains visible alongside it.
Original contributions
What was new, and what researchers did with it afterward.
Elkins and Chun established a reproducible framework for selecting, comparing, validating, and interpreting sentiment models across narrative texts.
Later uptake. Knight and Rocklage used emotional-arc analysis for narrative reversals in Science Advances; He, Breithaupt, Kübler and Hills grounded a 25,728-retelling study in the method in Scientific Reports; the Piper lab and NarraBench cite it in narrative-understanding research.
Later uptake. Floridi and Chiriatti cited its results within a year. Subsequent work in machine psychology, the AI Ghostwriter Effect, impersonation, literary memorization, and narrative bias tested or extended its behavioral questions.
Later uptake. Cugurullo and Xu cited the workflow in Policy and Society; surveys in Discover Applied Sciences and IEEE Access catalogued it; LREC workshop research and a Springer banking study applied its guidance in new settings.
Later uptake. Liu et al. (COLING 2025) adopted its confidence-scoring method for value-priority evaluation. Sowmya and Vasudeva replicated the eight-model audit design in IEEE Access; the Edinburgh LLM Ethics Whitepaper identified it as a method for probing ethical values through prompting.
Later uptake. Floridi and Ascani used the comparison in Minds and Machines; Lu and Tie adopted its regulatory-sandbox recommendation; later studies applied the three-regime map in law, political science, healthcare, sustainability, journalism, and AI safety.
Later uptake. Research on character dynamics in novel-to-film adaptation and computational narrative aesthetics now applies related affective measures across textual and audiovisual forms.
Methods adopted & extended
Researchers reused specific elements rather than merely citing the work.
Developed by Jon Chun, SentimentArcs is a novel ensemble method for comparing narrative sentiment trajectories across texts. Researchers have adopted ensemble comparison, smoothing, trajectory distance, and interpretive validation in corpus linguistics, translation, reader-response research, and narrative benchmarks.
The Aarhus group cited the Elkins–Chun study across work on Hemingway, Danish literature, and literary-quality prediction. Tilmatine and colleagues adopted its smoothing approach in Frontiers in Psychology; Aledavood used “middle reading” by name as a mixed methodology for digital humanities.
Can Sentiment Analysis Reveal Structure in a Plotless Novel? →
Yan Wang built a classical Chinese translation framework on Elkins’s Proust visualizations. Huang and Cheung cited the approach in Humanities and Social Sciences Communications; studies from three countries, including work on the Shahnameh, used it to compare human and AI translation. Nugraha, Asi & Fauzan cited the work in translation pedagogy as evidence for what AI can add to close reading.
Work traveling into new domains
Healthcare research has cited the regulatory comparison in work on trustworthy AI; medical education and global-health studies use the human-centered AI framework to examine disciplinary judgment and institutional design.
Sergio Arce García adapted emotional-structure analysis to disinformation assembled across posts and threads. Related methods appear in surveys of generative-AI misinformation, propaganda detection, and electoral communication.
Researchers applied emotional-arc methods to customer journeys at Harvard Business School, game-feel design in IEEE Transactions on Games, choreographic tension in dance education, and sentiment cycles in Chinese popular music.
Nielsen, Christensen and Bolwig selected narrative sentiment as one of two quantifiable approaches to human–nature narratives. Belhaouari and colleagues applied emotional-arc analysis to cyclical structure in Qur’anic Surah Yusuf.
Seyedtabatabaei and Fatemi cited Elkins’s independently authored The Shapes of Cinderella in their 2026 PLOS ONE study of the Cinderella complex and barriers to self-employment among rural women in Iran.
Independent work on Proust, memory, and lyric has been adopted in genetic criticism, psychoanalysis, philosophy, fashion studies, and comparative literature. Tom Stern engaged the Oxford volume on Proust in the European Journal of Philosophy.
AI evaluation & governance uptake
Named adoptions, replications, and tests of current AI work.
Liu et al.’s INVP framework directly adopted confidence scoring for LLM value-priority evaluation. Sowmya and Vasudeva replicated the audit across eight deployed models. Snoswell, Kilov and Lazar included it in their 2020–2025 map of LLM ethics evaluation, Beyond Verdicts.
Paris, Moon and Guo (FAccT 2025) identified the paper as one of three openness frameworks used in the field. The Model Openness Framework and a TMLR consensus paper used its analysis; Paris, Moon and Guo returned to it in Don’t Trust the Process (FAccT 2026), while Lee, Howison, Lee and Li tested openness claims against adoption on r/LocalLLaMA.
Near to Mid-term Risks and Opportunities of Open-Source Generative AI →
The EU–China–US framework appears in Communications of the ACM, PNAS Nexus, Nature Communications, Information Fusion, and comparative-law research. Eltohamy et al. used its account of the move from principles to binding legislation in low-carbon energy-systems research. Bai and Yao’s “Building the oracle” used it for Chinese newsroom AI; Bariach, Schoenegger, Bhaskar and Suleyman’s “Seemingly Conscious AI Risks” used it to compare national governance conditions.
Hedfeld’s “Language Models as a Challenge for Business Ethics” in Science and Engineering Ethics cited the audit to argue that ethical constraints on language models belong in institutional specification rather than ad hoc corporate discretion.
Teaching & field-building
The educational contribution was a research structure, not a generic AI-literacy program.
Kenyon describes the program founded by Elkins and Chun as the world’s first Human-Centered AI curriculum and lab. It brought disciplinary expertise into technical research through project-based courses, mentored collaboration, and a shared research lab.
UNESCO’s Prospects quoted its account of the digital humanities and AI. Later work applied the curricular framework in global health, chemistry, medical education, creative labor, digital archiving, and reviews of AI and Education 4.0. A Ukrainian-language university teaching book provided its first book-length, non-English uptake.
The curriculum produced mentored work in model evaluation, agents, narrative, creativity, and education, with student research reaching conferences, publications, datasets, and public tools. Terence Tao and Tanya Klowden later cited the program’s argument that disciplinary questions about knowledge, beauty, and meaning cannot be reduced to automation.
Selected reach
The maintained citation census records use across 15 fields and 55 subfields. That breadth supports the named examples above; it does not replace them.
Several works also lead their immediate publication venues in readership or citation, including Can GPT-3 Pass a Writer’s Turing Test?, The Shapes of Stories, and The Crisis of Artificial Intelligence. Counts change over time, so this page prioritizes traceable uses over rankings.
For the research itself, see Research. For longer-form work, see Books; for public discussion, see Speaking and Media.