为日语口语叙事构建首个系统化分析指南,提升研究可重复性。
Developing a Guideline for the Labovian-Structural Analysis of Oral Narratives in Japanese
- 基于劳博式框架,制定适配日语语法的分句规则
- 标注者在分句上达成高一致性(κ=0.80),结构分类中等一致
- 适用于日语质性研究者,尤其适合叙事分析初学者
叙事分析是定性研究的核心方法之一。主流的劳博式模型应用耗时耗力,需反复在局部与整体间往返解读。现有劳博数据集仅限英语,而日语在语法和话语习惯上差异显著。为此,本文提出首个针对日语文本的系统性劳博式分析指南,保留原六类结构,并针对日语特点提供明确的分句规则,涵盖更广泛的分句类型与叙事类型。使用该指南,标注者在分句任务上达成高一致性(Fleiss' kappa = 0.80),在两项结构分类任务中分别获得中等一致性(Krippendorff's alpha = 0.41 与 0.45),后者虽使用更细粒度划分仍略优于前人工作。本文详述模型、指南设计、标注流程及其应用价值,并讨论标注过程中的挑战及未来构建更大规模日语文本结构数据集的前景。
原文摘要 · Abstract (English)
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.
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