Developing a Guideline for the Labovian-Structural Analysis of Oral Narratives in Japanese

arXiv cs.CL / 4/1/2026

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Key Points

  • The paper introduces the first systematic guidelines for applying the Labovian-structural analysis model to Japanese oral narratives, filling a gap left by English-only datasets.
  • It keeps Labovian’s six categories while extending the framework with explicit, Japan-specific rules for clause segmentation to better match Japanese grammar and discourse.
  • The guidelines broaden both the clause types and narrative types covered, aiming to improve the applicability of Labovian analysis in Japanese qualitative research.
  • In annotation experiments, clause segmentation achieved high inter-annotator agreement (Fleiss' kappa = 0.80), while structural classification tasks showed moderate agreement (Krippendorff's alpha = 0.41 and 0.45).
  • The authors document the annotation process, challenges, and the utility of the guidelines, and they outline prospects for scaling toward a larger Japanese dataset for structural narrative analysis.

Abstract

Narrative analysis is a cornerstone of qualitative research. One leading approach is the Labovian model, but its application is labor-intensive, requiring a holistic, recursive interpretive process that moves back and forth between individual parts of the transcript and the transcript as a whole. Existing Labovian datasets are available only in English, which differs markedly from Japanese in terms of grammar and discourse conventions. To address this gap, we introduce the first systematic guidelines for Labovian narrative analysis of Japanese narrative data. Our guidelines retain all six Labovian categories and extend the framework by providing explicit rules for clause segmentation tailored to Japanese constructions. In addition, our guidelines cover a broader range of clause types and narrative types. Using these guidelines, annotators achieved high agreement in clause segmentation (Fleiss' kappa = 0.80) and moderate agreement in two structural classification tasks (Krippendorff's alpha = 0.41 and 0.45, respectively), one of which is slightly higher than that found in prior work despite the use of finer-grained distinctions. This paper describes the Labovian model, the proposed guidelines, the annotation process, and their utility. It concludes by discussing the challenges encountered during the annotation process and the prospects for developing a larger dataset for structural narrative analysis in Japanese qualitative research.