ethnographic fieldwork

This study addresses how dominant data storytelling practices often overlook the cultural contexts of communities with low digital literacy, thereby reinforcing their marginalization. Through a ten-month ethnographic investigation across three villages in Bangladesh, the research examines traditional performative forms—such as Puthi, Bhandari Gaan, and Pot—and how they interweave factual content, emotion, and aesthetics to convey moral lessons and historical memory. Building on these insights, the work proposes a novel data narrative paradigm that challenges Western-centric assumptions by integrating pluralistic voices, Indigenous ethical frameworks, and local artistic practices. This approach offers culturally sensitive and technologically adaptive design pathways for human-computer interaction (HCI) and data visualization, particularly suited to low-literacy communities.

This study addresses the limitations of conventional interview analysis, which relies heavily on researchers’ specialized qualitative skills, is difficult to scale, and often overlooks participants’ own interpretive logics. To overcome these challenges, the paper proposes the Documentary Mode of Interpretation (DMI), a membership-based method grounded in ordinary people’s natural language competencies. DMI identifies endogenous themes within interview texts to uncover the collective reasoning processes participants employ in making sense of the research topic. Crucially, this approach requires neither predefined coding frameworks nor formal training in social theory, thereby departing from traditional qualitative paradigms by generating themes bottom-up from participants’ perspectives. By significantly lowering the technical and epistemic barriers to qualitative analysis, DMI offers non-specialist researchers an accessible, low-threshold pathway to effectively interpret the collective meaning structures embedded in interview data.

Traditional questionnaires struggle to simultaneously capture qualitative depth and quantitative structure, limiting comprehensive understanding of complex social phenomena. This study proposes a dynamic survey platform powered by large language models (LLMs) that, for the first time, enables real-time semantic clustering of open-ended responses. Through an interactive feedback mechanism, users can rate, rank, and reflect on these clusters, generating visual reports that integrate qualitative insights with quantitative analysis. Innovatively embedding LLMs within a closed-loop data collection framework, the approach facilitates dynamic comparisons between individual perspectives and group-level trends. Empirical validation across two field studies involving 93 participants demonstrates that the platform significantly enhances data richness and user engagement compared to conventional survey tools, while effectively fostering collaborative sensemaking.

Traditional narrative research imposes dual cognitive burdens on both researchers and participants through labor-intensive hand-drawn storytelling and member-checking procedures. Method: This paper introduces NAME, a novel paradigm that pioneers the integration of visual generative modeling into narrative inquiry. NAME employs a generative module jointly conditioned on character positioning and shape constraints to enable end-to-end translation from textual narratives to coherent, semantically grounded illustrations. A three-dimensional evaluation framework—assessing fidelity, narrativity, and interpretability—is proposed to rigorously quantify performance while reducing data dependency and cognitive load. Results: Experiments demonstrate substantial improvements: FID drops from 195 to 152 using only 0.96% of training data; further reductions to 152 and 49 are achieved under 70:30 and 95:5 train-test splits, respectively; and the proposed narrativity metric attains 3.62—significantly surpassing the baseline of 2.66. NAME enhances both analytical efficiency and participant inclusivity in narrative research.

Qualitative research faces a persistent trade-off between analytical scale and interpretive depth. Method: This paper introduces Neo-Grounded Theory—a novel methodology that integrates high-dimensional semantic embeddings (1536-D), hierarchical vector clustering, and a multi-agent collaborative coding system into the grounded theory framework, thereby unifying computational objectivity with humanistic interpretability. It supports both fully automated and human-guided human–AI collaborative analysis modes, enabling detection of latent patterns—such as identity bifurcation—that elude manual identification. Contribution/Results: Empirical evaluation demonstrates a 168× acceleration in analysis time (3 hours vs. 3 weeks), a 96% cost reduction ($50,000 → $500), intercoder reliability of 0.904 (Cohen’s κ), and generation of actionable dual-path theoretical models. This work advances qualitative research toward real-time, reproducible, and democratized paradigms.

Existing tools neglect the central role of narrative in data analysis, resulting in non-traceable reasoning, fragmented reflection, and incoherent explanations. This paper introduces the “narrative-as-interface” paradigm, proposing an exploratory framework where narrative generation serves as the primary interaction mechanism—integrating questioning, visualization, reflection, and explanation into an iterative narrative flow. Key contributions include: (1) semantic-aligned automatic view generation; (2) a narrative-first input interface; (3) insight provenance modeling; and (4) inquiry lifecycle tracking. A user study (N=20) demonstrates a 37% increase in exploration breadth and a 2.1× improvement in reflection depth. Expert evaluation (N=6) confirms 92% alignment in narrative intent, substantiating significant advances toward traceable, defensible, and intention-aware data analysis practices.

本文通过结合舞蹈人类学、感官民族志和设计的方法，利用可穿戴技术激活体感系统，以改善创意制作过程中的记录与反思。

This study addresses the lack of systematic understanding regarding the types and motivations of visual representations in qualitative research. Building upon and extending Verdinelli & Scagnoli’s (2013) work through a data-driven literature review, it conducts a content analysis of articles and their visualizations published between 2020 and 2022 in three leading qualitative methods journals. Integrating epistemological stance classification with visualization-type coding, the study innovatively combines correspondence analysis and cognitive network analysis for the first time. Findings indicate that while visualizations remain underutilized in qualitative research, their typological diversity is increasing, and the choice of graphical representation appears largely independent of the authors’ epistemological positions. These results offer both empirical grounding and methodological innovation for integrating interdisciplinary visualization tools into qualitative inquiry.

This study addresses a critical gap in existing qualitative analysis tools, which often prioritize efficiency and consensus at the expense of reflexivity, traceability of analytical processes, and constructive disagreement. To redress this imbalance, the authors design and implement Reflexis, a collaborative analysis workspace that systematically integrates reflexivity, visualizations of code evolution, and stance-aware dialogue into its core architecture. Through embedded reflective prompts, transparent tracking of coding trajectories, and scaffolding for stance-aware discussions, Reflexis facilitates rigorous and nuanced collaborative interpretation. A user study with twelve participants demonstrates that the tool effectively fosters fine-grained reflection, transforms disagreements into productive dialogue, and reveals emergent design requirements such as higher-order memo networks and timely proactive reminders.

Existing computational tools for qualitative data analysis often fall short in effectively supporting causal exploration due to insufficient contextual awareness, limited trustworthiness, or overly complex outputs. To address these limitations, this work proposes QualCausal, the first interactive causal analysis system grounded in user research–driven design principles. Developed through formative user studies, QualCausal integrates context-aware processing, cognitive scaffolding, and explainability mechanisms to facilitate efficient exploration and validation of causal hypotheses within qualitative datasets. The system enables researchers to extract causal relationships, construct interactive causal networks, and examine findings through coordinated multi-view visualizations. User evaluations demonstrate that QualCausal significantly reduces analytical burden, provides robust cognitive support, and prompts critical reflection on how computational tools can be meaningfully integrated into social science research practices, thereby bridging the gap between computational assistance and qualitative inquiry paradigms.