| Roald Hoffmann, “The Tensions of Scientific Storytelling” (American Scientist, 2014) | Adopt as practice grounding: scientific narratives order calculations, failed attempts, mechanisms, unresolved tensions, and discoveries rather than merely decorating results. | Grounds the discovery-order worked case and the need to retain unresolved tension and source return. | Historical practice anchor, not current cognitive SoTA or authority over FPF ethics. |
Wolf Schmid, Narratology: An Introduction (2010), and Matei Chihaia, Introductions to Narratology: Theory, Practice and the Afterlife of Structuralism (2012) | Adapt source material, selection, composition, order, viewpoint, and presentation as domain distinctions. | Grounds the ordering/connective account and viewpoint-sensitive loss. | Historical domain anchors; fiction-specific vocabulary does not become FPF Core ontology. |
| Tan T. Nguyen, “A Review of Mechanistic Models of Event Comprehension” (2024); Lijuan Chen and Xiaodong Xu, “Neural and Behavioral Evidence for Differential Processing of Narrative Perspective in Novel Reading” (2026); Christoph Mengelkamp, Stefanie Golke, and Markus Appel, “Effects of Reading Goal Instructions on the Comprehension and Metacomprehension of Informative Narratives” (2025); Antonios Georgiou, Tankut Can, Mikhail Katkov, and Misha Tsodyks, “Large-scale study of human memory for meaningful narratives” (2025) | Adopt as current cognitive pressure for event models, prediction and update, reading-goal effects, reconstruction, memory loss, metacomprehension error, and viewpoint-sensitive recovery. | Supports triggered event-model/viewpoint fields, reader-use entry, source comparison, and return. | These studies inform narrative use; they do not supply evidence, assurance, ethics, or policy authority for a particular narrative. |
Albert Gatt and Emiel Krahmer, “Survey of the State of the Art in Natural Language Generation” (2018); Amal Alabdulkarim, Siyan Li, and Xiangyu Peng, “Automatic Story Generation: Challenges and Attempts” (2021); Rogelio E. Cardona-Rivera, Arnav Jhala, Julie Porteous, and R. Michael Young, “The Story So Far on Narrative Planning” (2024); Vikram Kumaran, Jonathan Rowe, Bradford Mott, and James Lester, “SceneCraft: Automating Interactive Narrative Scene Generation in Digital Games with Large Language Models” (2023), DOI 10.1609/aiide.v19i1.27504; Yuan Ma, Richard Susilo, Patrik Haslum, and Hanna Suominen, “Text-to-Text Automatic Story Generation: A Survey” (2026); Aynigar Rahman, Aihe Yu, and Kyungeun Cho, “Game Knowledge Management System: Schema-Governed LLM Pipeline for Executable Narrative Generation in RPGs” (2026) | Adopt content and narrative planning, grounding, controllability, schema constraints, repair, and evaluation limits for automated cases. | Grounds the generated event-graph case, generated-fluency boundary, source comparison, and conditional C.35 architecture-use exit. | The 2018/2021 surveys are historical anchors; the 2024/2026 planning, survey, and schema-governed work represents the current line used here. |
Melanie C. Green and Timothy C. Brock, “The Role of Transportation in the Persuasiveness of Public Narratives” (2000); Michael F. Dahlstrom and Shirley S. Ho, “Ethical Considerations of Using Narrative to Communicate Science” (2012); Hanna Meretoja, “Narrative and Human Existence: Ontology, Epistemology, and Ethics” (2014, background only); FPF D.1 through D.5 | Adapt engagement as a real effect with a bounded-use and ethical boundary. | Grounds the engagement check and the anti-pattern against treating engagement as evidence or permission. | Historical/background anchors. Current evidence, assurance, ethics, and policy claims still require their own exact sources and FPF patterns. |