Organizations: The University of Tokyo, Tokyo, Japan · Riken, Tokyo, Japan · Kobe University, Hyogo, Japan · Ritsumeikan University, Kyoto, Japan · Keio University, Tokyo, Japan
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.
Figures & tables
#
Caption
Story
Micro
Macro
1
When I finally have time for myself, that is when I feel most fulfilled.
S
F
Abstract
2
Last month I went out to a small mountain town.
F
Orientation
3
I bought a lunch at the station
N
Complication
4
and took the train out there.
N
Complication
5
The sky was bright blue,
F
Orientation
6
and I felt completely free.
R
Evaluation
Table 1: This table presents an example of clause segmentation and functional classification, based on an anonymized and extensively modified interview transcript translated from Japanese. Each clause is annotated with narrative span boundaries, as well as micro- and macro-level functional labels. Story: S/E indicates the starting and ending points of the narrative of the given type (Story, in this case). Columns corresponding to Habitual and Hypothetical narratives are omitted from this table for brevity. Micro labels: N = Narrative, R = Restricted, F = Free. This excerpt was produced in response to a question asking when the speaker feels a sense of satisfaction or happiness.
Label
Definition
Total
Abstract
A short preview indicating the content or point of the story. Abstracts are often missing, like Codas, but when present they introduce the upcoming narrative in advance.
17
Orientation
Introduces characters, time, place, or situation. Orientations guide the listener from the present moment into the past scene in which the story takes place.
134
Complication
Reports “what happened”—the sequence of past events that forms the core of the story. Complications may be interrupted by Evaluation or Orientation clauses.
172
Evaluation
Provides the narrator’s stance, emotions, or interpretation of events, emphasizing why the story is worth telling.
42
Resolution
Describes the outcome that concludes the chain of Complications.
16
Coda
Returns the narrative to the present and signals its end, often explaining how the reported events continue to affect the present.
18
Table 2: Macro-structural labels with total clause counts.
Label
Definition
Narrative span
Mean length
Story
A sequence of events that happened once at a specific time in the past.
17
15.41
Habitual
Describes repeated or customary actions, routines, or recurring events.
16
9.31
Hypothetical
Describes imagined or counterfactual events that did not actually occur.
1
6.00
Table 3: Narrative span categories with definitions, number of spans, and mean clause counts with in a span in our dataset.
Figure 1: Micro-label determination chart across narrative clause types.
Micro label
Rate
Narrative (N)
0.52
Free (F)
0.15
Restricted (R)
0.08
Table 4: Exact-match rates for micro and macro labels ( P(match∣chosen) ). Highest values are in bold.
Department of Computer Science, University of Hamburg · Department of Socioeconomics, University of Hamburg · AI and Explanability Group, Leuphana University of Lüneburg