Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Lingua Technica: Journal of Digital Literary Studies Lingua Technica: Journal of Digital Literary Studies
Seroja Ainun Nadhifah
Unknown Affiliation
Arts Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
The style that was learned: large language models, imitation, and the ethics of literary influence Nomvuselelo N. Nxumalo; Seroja Ainun Nadhifah
Lingua Technica: Journal of Digital Literary Studies Vol. 2 No. 2 (2026): Meta-literature: between authorship and machine-mediated literary creativity
Publisher : Asosiasi Relawan dan Pengelola Jurnal LPTNU (ARJUNU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64595/80qxyz22

Abstract

Background: Large language models have transformed literary imitation into a scalable practice, complicating distinctions among stylistic influence, textual reproduction, authorship, and ethical appropriation. Objective: This study examines how author-conditioned generation produces stylistic convergence, preserves or transforms source relations, and acquires ethical legitimacy under documented research conditions. Method: This study applies paired stylometry, intertextual reproduction auditing, and an ethical literary influence matrix to ten public-domain literary excerpts, ten controlled model outputs, five methodological documents, and seven contextual sources. Results: Stylometric comparison reveals graded convergence, with recognizable authorial cues coexisting with substantial divergence in sentence architecture and other distributed features. Reproduction auditing identifies low lexical overlap, no shared four-grams, no source-character or source-event transfer, and consistently high transformation across all pairs. Ethical coding classifies risk as low because sources are public domain, authors are deceased, prompts and exemplars are disclosed, and human control is documented. Implication: These findings indicate that literary resemblance should not be treated automatically as authorship, memorization, plagiarism, or infringement, but evaluated through layered textual and contextual evidence. Novelty: This study advances a multidimensional framework that theorizes learned style as mediated literary influence shaped by convergence, transformation, provenance, and accountable use across computational, literary, legal, and contemporary ethical domains.
The style that was learned: large language models, imitation, and the ethics of literary influence Nomvuselelo N. Nxumalo; Seroja Ainun Nadhifah
Lingua Technica: Journal of Digital Literary Studies Vol. 2 No. 2 (2026): Meta-literature: between authorship and machine-mediated literary creativity
Publisher : Asosiasi Relawan dan Pengelola Jurnal LPTNU (ARJUNU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64595/80qxyz22

Abstract

Background: Large language models have transformed literary imitation into a scalable practice, complicating distinctions among stylistic influence, textual reproduction, authorship, and ethical appropriation. Objective: This study examines how author-conditioned generation produces stylistic convergence, preserves or transforms source relations, and acquires ethical legitimacy under documented research conditions. Method: This study applies paired stylometry, intertextual reproduction auditing, and an ethical literary influence matrix to ten public-domain literary excerpts, ten controlled model outputs, five methodological documents, and seven contextual sources. Results: Stylometric comparison reveals graded convergence, with recognizable authorial cues coexisting with substantial divergence in sentence architecture and other distributed features. Reproduction auditing identifies low lexical overlap, no shared four-grams, no source-character or source-event transfer, and consistently high transformation across all pairs. Ethical coding classifies risk as low because sources are public domain, authors are deceased, prompts and exemplars are disclosed, and human control is documented. Implication: These findings indicate that literary resemblance should not be treated automatically as authorship, memorization, plagiarism, or infringement, but evaluated through layered textual and contextual evidence. Novelty: This study advances a multidimensional framework that theorizes learned style as mediated literary influence shaped by convergence, transformation, provenance, and accountable use across computational, literary, legal, and contemporary ethical domains.
Co-Authors Nomvuselelo N. Nxumalo