Beyond Abducted Semantics: Ethnographic Methods and Literary Theory as Frameworks for Research Engines That Enhance Human Understanding

This article examines how ethnographic methodology and literary theory can advance research engines and artificial intelligence systems beyond the reductive computational approaches that dominate contemporary AI development. Drawing on recent Stanford research revealing fundamental gaps in large language models’ ability to distinguish factual knowledge from belief, I argue that contemporary AI systems enact what I term “abducted semantics”—appropriating the inferential logic of human meaning-making while systematically attenuating the culturally embedded, phenomenologically grounded capacities that generate authentic understanding. Through close analysis of Clifford Geertz’s thick description, Charles Sanders Peirce’s triadic semiotics, and canonical literary works—Miguel de Cervantes’ <i>Don Quixote</i> and Gabriel García Márquez’s <i>One Hundred Years of Solitude</i>—I demonstrate that human understanding operates through complex semiotic processes irreducible to pattern-matching and statistical prediction. The article proposes concrete interventions to transform research engines from tools of semantic extraction into technologies that preserve and enhance interpretive richness, arguing that ethnographic and literary methodologies offer essential correctives to the epistemological impoverishment inherent in current AI architectures.