000K  utf8
1500  eng
2050  urn:nbn:de:hbz:468-20261008-114141-5
2051  10.25926/c5vp-bm59
3000  Lutostański, Bartosz
4000  Structures of Signs without Cultural Knowledge. Narrative Competence and Cultural Specificity in AI-Generated Fiction  [Lutostański, Bartosz]
4209  This article argues that narrative competence and cultural competence are independent variables in AI-generated fiction: a text can be mimetically vivid, synthetically controlled, and thematically coherent while getting its cultural and historical substance precisely wrong. To demonstrate this, it compares two systems – ChatGPT, a globally trained commercial model, and PLLUM, a Polish academic model – across a corpus of ten prompts requiring short stories set in post-1939 Polish contexts, with the culturally decisive reference embedded in the situation rather than stated. The method combines two instruments applied in sequence: James Phelan’s tripartite (mimetic, synthetic, thematic) model, adapted posthumanistically to treat AI outputs as “structures of signs” rather than rhetorical acts; and a cultural competence rubric assessing factual accuracy, nuance, contested material, and depth of embedding. Two case studies – a 1967 Krak.w scene and a contemporary scene turning on the rapper Mata’s Patointeligencja – yield a typology of four failure modes: Generic Approximation, Emotional Inversion, Content Substitution, and Perspectival Misreading. The discussion proposes a distinction between cultural familiarity and historical situatedness, and argues that the transparency asymmetry between academic and commercial AI is constitutive of the question of whose cultural knowledge these systems encode.
4950  https://doi.org/10.25926/c5vp-bm59$xR$3Volltext$534
4950  https://nbn-resolving.org/urn:nbn:de:hbz:468-20261008-114141-5$xR$3Volltext$534
4961  https://elpub.bib.uni-wuppertal.de/receive/duepublico_mods_00000883
5051  800