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

TL;DR

This paper introduces systematic guidelines for applying Labovian narrative analysis to Japanese oral narratives, adapting the model to Japanese grammar and discourse conventions, and demonstrates high annotation agreement.

Contribution

It provides the first explicit, rule-based framework for Labovian narrative analysis in Japanese, extending existing models and enabling consistent annotation.

Findings

High agreement in clause segmentation (Fleiss' kappa = 0.80)

Moderate agreement in structural classification tasks (Krippendorff's alpha = 0.41 and 0.45)

Guidelines facilitate reliable annotation of Japanese narratives

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.