Scholarly Reception

Across indexes: 918 citations on Google Scholar; citing works span fifteen fields and sixty-one subfields, with 2025 the largest year on record and 2026 pacing ahead of it.

AI safety, ethics, and governance

Informed AI Regulation: Comparing the Ethical Frameworks of Leading LLM Chatbots (Chun and Elkins, 2024)

The first ethics-based audit of moral reasoning in deployed LLMs — the paper that operationalized ethical evaluation as a systematic audit — and now the anchor of a strand of AI-safety evaluation work. Liu et al.’s INVP framework (COLING 2025) directly adopts its confidence-scoring methodology for evaluating LLM value priorities; Sowmya and Vasudeva (IEEE Access, 2026) replicate the eight-model audit design outright. At AIES, Jain, Calacci and Wilson cite its central finding — that LLM ethical reasoning shows a clear bias toward societal and cultural norms — while Rathje’s “Learning When Not to Measure” and Hey, Walsh and Mustafaraj’s human-versus-LLM comparison engage the framework, and the Edinburgh LLM Ethics Whitepaper names it a representative methodology for probing ethical values through prompting. Snoswell, Kilov and Lazar include it among 69 works surveyed in “Beyond Verdicts,” their map of LLM ethics evaluation from 2020 to 2025.

Near to Mid-term Risks and Opportunities of Open-Source Generative AI (Eiras et al., ICML 2024 oral)

This position paper set the benefit–risk terms of the open-model governance debate. Taeihagh’s “Governance of Generative AI” (Policy and Society, 2025) — the field’s most-cited survey — carries its access-spectrum analysis; Liesenfeld and Dingemanse build on it in their FAccT “open-washing” critique of the EU AI Act; and the Model Openness Framework (White, Haddad, Osborne et al.) cites its systematic conclusion that the benefits of open release outweigh the risks. Paris, Moon and Guo (FAccT 2025) name it one of three openness frameworks structuring the field, and Casper, O’Brien, Longpre and colleagues cite it in their TMLR consensus paper on open-weight model risk — authored by safety researchers across MIT CSAIL, Stanford, Mila, Carnegie Mellon, Princeton, the UK AI Security Institute, and Hugging Face. Empirical and policy uptake extends from the Hugging Face development-activity study (Journal of Computational Social Science) to “An FDA for AI?” (AIES) and DeepSeek-era analyses of open-model competition.

Comparative Global AI Regulation: Policy Perspectives from the EU, China, and the US (Chun, Schroeder de Witt, Elkins, 2024)

The first systematic EU–China–US regulatory comparison after the EU AI Act’s passage. Its three-regime map — horizontal risk-based, market-driven, state-led vertical — has become standard shorthand in the citing literature. Floridi and Ascani cite it in Minds and Machines on AI for legislative innovation in the Italian Parliament; Olugbade engages it with six in-text citations across a Global Public Policy and Governance argument for a global governance mechanism; and the paper’s regulatory-sandbox recommendation is adopted in Lu and Tie’s ASEAN–EU comparison. Uptake runs through Communications of the ACM (“AI Regulation in U.S. States”), JCMS on EU digital sovereignty, Information Fusion on trustworthy AI in healthcare, FAccT red-teaming of the AI Act, and an empirical ECML/PKDD study of LLM censorship that adopts its account of China’s top-down model — with comparative-law engagement from Brazil, Serbia, Indonesia, Korea, and the Philippines.

Computational humanities and narrative

Can GPT-3 Pass a Writer’s Turing Test? (Elkins and Chun, Journal of Cultural Analytics, September 2020)

The first writer’s Turing test of a large language model — published within months of GPT-3’s release, and cited within the year by Floridi and Chiriatti for the empirical claim that “GPT-3 writes better than many people.” The paper now sits in the reference layer of the LLM literature, and both sides of the capability debate quote it: optimists cite its evidence that models imitate authors’ styles and produce nuanced argument (Lenci credits it with making the classical imitation-game Turing test “obsolete or at least ineffective”), while skeptics — Sap, Le Bras, Fried and Choi at EMNLP, among others — cite its evidence that LLMs fail at higher-order reasoning. It appears in PNAS (Mei et al.’s behavioral Turing test), Science Advances (Spitale et al. on GPT-3 disinformation), the most-cited paper on LLMs in education (Kasneci et al.), Hagendorff and colleagues’ machine-psychology program, Draxler and colleagues’ AI Ghostwriter Effect study in ACM TOCHI, and NeurIPS, EMNLP and NAACL work on impersonation, literary memorization, and narrative bias. In literary studies, N. Katherine Hayles engages it in Bacteria to AI (2025) and in New Literary History, and Martin Paul Eve cites it across two books. Beyond that core, the citation census records uptake in medicine, law, legislative studies, forensics, engineering, and grief-tech studies — the paper scholars in any field reach for when they need an authority on what early LLMs could and could not do as writers.

The Shapes of Stories: Sentiment Analysis for Narrative (Cambridge University Press, 2022)

The first methodology for sentiment analysis of narrative: an ensemble approach benchmarking more than three dozen models and demonstrating how smoothing, model selection, and interpretation determine what an emotional arc reveals. The sciences now test its claims at scale: Knight and Rocklage’s Science Advances study of narrative reversals and He, Breithaupt, Kübler and Hills’s 25,728-retelling study in Scientific Reports both ground their designs in emotional-arc analysis. In NLP, the book is core infrastructure for the leading computational-narrative groups — the Piper lab cites it across NarraBench (EACL 2026) and four further papers on narrative understanding, and Tian and Huang’s EMNLP benchmark on human-level narrative generation carries it as a methodology-level citation. The method has traveled well beyond literature: customer-journey research at Harvard Business School, game-feel design in IEEE Transactions on Games, choreographic tension mapping in dance education, sentiment cycles in Chinese pop lyrics, and sustainability science, where Nielsen, Christensen and Bolwig name it one of two quantifiable methods that could scale their comparative model of human–nature narratives. Within literary studies it is adopted across the Cambridge Elements in Digital Literary Studies series and surveyed as the field reference in Digital Humanities Quarterly.

Can Sentiment Analysis Reveal Structure in a Plotless Novel? (Elkins and Chun, 2019) and “middle reading”

This paper introduced “middle reading” and was the first to test whether sentiment methods survive nonlinear narrative — the question computational literary studies still benchmarks against. The Aarhus group — Bizzoni, Feldkamp, Moreira, Nielbo, Thomsen — cites “Elkins and Chun 2019” across at least six papers on Hemingway, Danish literature, and literary-quality prediction as the standing reference for that question; Sui, Hamilton and colleagues call nonlinearity “a hard problem for narratology, by both computational (Elkins and Chun, 2019) and traditional approaches.” Jacobs engages the method in Neurocomputational Poetics, Tilmatine and colleagues adopt its smoothing approach in reader-response experiments in Frontiers in Psychology, and Aledavood takes up “middle reading” by name as a model for mixed methodology in the digital humanities. Cross-disciplinary travel reaches Chinese science fiction, Romanian novel annotation, and tourism branding.

eXplainable AI with GPT-4 for Story Analysis (Chun and Elkins, International Journal of Digital Humanities, 2023)

The journal’s special issue on Reproducibility and Explainability names the paper as proposing “an advanced XAI approach and workflow for LLM-based research.” Cugurullo and Xu carry it into political theory in Policy and Society, citing it as the authority on LLMs’ opaque epistemology in their widely cited “When AIs become oracles”; surveys in Discover Applied Sciences and IEEE Access catalogue it as the exemplar of GPT-4 story analysis; LREC workshop research adopts its methodological guidance for literary-historical sentiment analysis; and a Springer banking case study applies the framework in industry.

In Search of a Translator (Elkins, Frontiers in Computer Science, 2024)

Translation studies has taken the paper up as a method for measuring affective fidelity. Yan Wang’s computational-linguistics framework for classical Chinese translation is built explicitly on its Proust emotional-arc visualizations; Huang and Cheung cite it in Nature portfolio’s Humanities and Social Sciences Communications on AI literary translation; and evaluation frameworks and human-versus-AI studies from three countries — including work on the Shahnameh — engage its approach.

AI, education, authorship, and the university

The Crisis of Artificial Intelligence: A New Digital Humanities Curriculum for Human-Centred AI (Chun and Elkins, 2023)

The first human-centered AI curriculum in the digital humanities, and the channel through which it entered education policy, mathematics, and global health. Terence Tao (with Tanya Klowden) draws on it for the observation that the tasks AI automates “did not require an understanding of more philosophical aspects of a profession, such as the nature of knowledge, beauty, meaning.” UNESCO’s Prospects quotes its diagnosis of the “AI crisis” in the digital humanities; the Frontiers in Education review of AI and Education 4.0 builds on the curriculum framework; and engagement runs through AIES work on bias in computing, a 29-author global-health pedagogy study in BioData Mining, the Journal of Modern Italian Studies’ “Mussolini and ChatGPT,” and Dalsgaard’s analysis of GenAI and creative labor. The argument has even returned as a banner: a 2025 humanities-journal essay titled “What AI can’t Teach: The Return of the Humanities” cites it.

AI Comes for the Author (Poetics Today, 2024) and What the Rise of AI Means for Narrative Studies (Narrative, 2022)

The narrative-theory interventions have drawn direct response from the field’s leaders: James Phelan engages the Poetics Today essay as part of an “instructive debate” in the same journal. The Narrative essay is quoted in philology (Forum for Linguistic Studies, on the risk of biased generation) and in philosophy of mind (Futurity Philosophy, on its critique of rigid symbolic methods).

A(I) University in Ruins (PMLA, 2024)

Cao and colleagues anchor a central theoretical claim on the article — “An ‘objective’ model, according to Katherine Elkins, cannot exist” — and within a year of publication it had crossed into German studies via Imke Meyer’s “Ghosts in the Machine: Kafka and AI” and into information-systems research on GenAI-mediated learning.

Beyond Plot: How Sentiment Analysis Reshapes Our Understanding of Narrative Structure (Journal of Cultural Analytics, 2025)

Within months of publication, the article became the theoretical warrant for a public argument: Hannah H. Kim’s Los Angeles Review of Books essay “When Story Loses the Plot” pivots on it — “Literary theorist Katherine Elkins suggests that emotional structure may be more fundamental than plot structure since ‘plotless’ narratives can have emotional arcs that create key moments and produce momentum.” Scholarly uptake is under way in the Journal of Computational Social Science, on character dynamics in novel-to-film adaptation.

Literary scholarship and philosophy

The earlier comparative-literature work continues to earn citations decades on. “Stalled Flight” (Comparative Literature Studies, 2002; A. Owen Aldridge Prize) is cited by Sonam Singh in Comparative Literature and by Marko Marinčič in Acta Neophilologica as the model reading of Horace 2.20 behind Baudelaire’s “Le Cygne.” “Middling Memories” (Discourse, 2002) has traveled furthest: its non-archival memory framework is adopted by Alison Luyten in the Antwerp genetic-criticism ecosystem, engaged by Taylor Johnston in Critique on Knausgaard’s deconstruction of Proustian memory, taken into psychoanalysis by Juhani Ihanus in the International Forum of Psychoanalysis, and carried into Pamuk and Durrell studies. “Naming the Lyric” (Philosophy and Literature, 2020) is cited in Rick Anthony Furtak’s Kierkegaard, Socrates, and the Meaning of Life (Cambridge University Press, 2025), and the edited volume Proust’s In Search of Lost Time: Philosophical Perspectives (Oxford University Press, 2022) is extensively engaged in Tom Stern’s “Proustian Grief” in the European Journal of Philosophy.

This page is the detailed record of scholarly reception — verified citations, named citers, and how the work has been taken up. For an at-a-glance map of the work by topic, see Research.