Writing for Machines, Formatting Originality: Plagiarism Detection and the Automation of Authorship

This article examines the institutional, technical, and ontological consequences of plagiarism detection platforms on contemporary academic writing. It argues that services like Turnitin and Grammarly do not simply detect plagiarism but actively reconfigure what counts as authorship, originality, and intellectual integrity. Drawing on historical and media-theoretical perspectives, the article traces plagiarism’s emergence as a culturally contingent offense, showing how its definition has shifted from oral cultures all the way to algorithmically processed patterns. In this transformation, plagiarism detection becomes less a neutral evaluative tool than a form of infrastructural governance that translates interpretive judgment into machinic legibility. Drawing on theorists such as Foucault, Chun, Kittler, Flusser, and Byung-Chul Han, the article situates plagiarism detection within a broader shift from expressive authorship to operational formatting. The article argues that plagiarism detection tools function as psychopolitical instruments, reshaping academic labor through anticipatory compliance and rendering originality as machinic compatibility.