ARCHETYPAL ANALYSIS OF STOCHASTIC TEXTS: A DIALOGICAL HUMAN-ALGORITHMIC APPROACH

Abstract After the GPT-3 boom, there is an ongoing debate regarding the language heuristics in automatic text generation, as well as their usefulness for scientific inquiry. In this article, we conduct a computational study that engages in a discursive problematization of textual analysis methods, seeking to reconcile epistemological tensions between positivist and interpretive paradigms in Psychology, Literature, and Computer Science. For our “experiment”, (a) a pseudo-random probabilistic language model is trained on a classical narrative works containing epic and heroic traits; (b) a stochastic text is generated using n-gram modeling in Python; (c) the output text is interpreted using Analytical Psychology and Literary Criticism; (d) an integrative discussion reconciles the previous steps, suggesting a new methodological approach for ideation and theorizing in scientific endeavors. We conclude the study optimistically, highlighting the benefits of this mode of inquiry for social psychology.

• Publication year

  2025

• Venue

  Psicologia & Sociedade

• Publication date

  Unknown publication date

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1590/1807-0310/2025v37287281
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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