Large-Scale Qualitative Research with AI: Infrastructure, Management and Operation of the Socioscope Data Pipeline

The Socioscope project introduces a pioneering approach to Large-Scale Qualitative Research (LSQR) by collecting extensive, open-ended, multimedia field data across hundreds of cases within the food system domain. It leverages Artificial Intelligence (AI) to enable scalable analysis of this material. This initiative outlines the technical infrastructure and management of its data pipeline, detailing the systematic collection and processing methods for AI-augmented analysis.

Why it matters

This research demonstrates a novel application of AI to qualitative data analysis at an unprecedented scale, offering a robust framework for understanding complex systems like the food supply chain. Such methodologies can significantly enhance data-driven decision-making and policy formulation by providing granular insights from diverse, real-world data.

Key insights

• The Socioscope project employs Large-Scale Qualitative Research (LSQR) methodologies.
• It collects comparable, open-ended, and multimedia field data.
• Data is gathered from hundreds of cases, specifically within the food system domain.
• AI is utilized to make the collected material analyzable at scale.
• The project documents entities like farms, processors, distributors, retailers, restaurants, as well as influencing actors such as municipalities, government programs, banks, NGOs, and universities.
• The paper focuses on the technical reference for building and managing the data corpus to facilitate AI-augmented analysis.
• It describes the end-to-end data pipeline, including the systemic sampling frame and transaction grid.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.29751