Orchestrated Distant Reading: A Generative Pre-trained Transformer (GPT)-Based Modular Workflow for Traceable Life-History Analysis

BackgroundQualitative social research faces a persistent methodological problem: how to move from lived narrative to analytical reconstruction without making that movement opaque.This is especially relevant in life-history research, where empirical material is dense, temporally layered, relational, and retrospectively organized.Existing Large Language Model (LLM)-assisted qualitative approaches have advanced tasks aligned with thematic analysis, such as coding, categorization, summarization, and pattern identification.However, they do not fully address life-history analysis, which requires reconstructing biographical temporality, narrative sequence, actor-event relations, and links between singular trajectories and broader social processes.Orchestrated Distant Reading (ODR) addresses this gap by organizing LLM assistance into a modular, staged, and researcher-verified workflow that makes each analytical transformation explicit. MethodsThis article introduces ODR, a generative pre-trained transformer (GPT)-based modular workflow for life-history analysis.The modules were selected according to the operations required to move from biographical narrative to traceable reconstruction: transcript cleaning, structured extraction, lexical and semantic mapping, construction of a project temporal baseline, interview temporal alignment, narrative profiling, cross-case comparison, inter-block synthesis, and controlled academic writing.During development, each module's instructional design was tested and refined to identify stable task definitions, input requirements, and output structures capable of producing satisfactory intermediate results under human review.The workflow was initially applied to two life-history interviews from a qualitative study on public interventions in coffee-growing territories in Chiapas, Mexico. ResultsThe initial application showed that dense life-history interviews could be transformed into a traceable analytical sequence without collapsing interpretation into a single generative response.The workflow separated lexical recurrence, temporal reconstruction, actor-event relations, narrative profiling, and cross-case comparison.As procedural indicators, each module generated a predefined intermediate output linked to a specific input source, and each output was subject to a documented validation decision before reuse.This made it possible to distinguish biographical temporality from project-level historical chronology, identify mid-level comparative patterns, and preserve asymmetries between cases.Human verification remained central to preventing unsupported inference.