An End-to-End Workflow for Processing Multilingual Stakeholder Workshop Data: A Soil Health Case Study

Vollständiger Abstract

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Stakeholder workshops often produce diverse qualitative and ordinal data that are difficult to process consistently, transparently, and reproducibly, particularly in multilingual settings. To address these challenges, we developed an end-to-end workflow for systematic processing of multilingual participatory workshop data. The workflow integrates preprocessing, structured data management, computational analysis, automated reporting, and interactive dissemination. It incorporates a range of data analysis methods, including large language models (LLMs), and supports both qualitative exploration and quantitative comparison of stakeholder perspectives. We also propose an LLM-based approach for topic extraction and intensity scoring, which transforms qualitative workshop inputs into quantitative representations. The workflow is demonstrated in the EU BENCHMARKS project, which involves multiple workshops, stakeholder groups, land-use contexts, and languages. The main contribution of this work is a transparent and adaptable workflow for systematic processing of multilingual participatory workshop data, supporting reproducible analysis, scalable dissemination, and cross-workshop comparison.