On the performativity of SDG classifications in large bibliometric databases
This work addresses issues for researchers and institutions relying on bibliometric data for impact measurement, but it is incremental as it builds on existing critiques of SDG classifications.
The study tackled the problem of performativity and bias in large bibliometric databases due to divergent UN Sustainable Development Goals (SDG) classifications, finding that fine-tuned large language models (LLMs) exhibit high sensitivity and arbitrariness in model architecture, publications, fine-tuning, and generation, raising concerns about their use in research.
Large bibliometric databases, such as Web of Science, Scopus, and OpenAlex, facilitate bibliometric analyses, but are performative, affecting the visibility of scientific outputs and the impact measurement of participating entities. Recently, these databases have taken up the UN's Sustainable Development Goals (SDGs) in their respective classifications, which have been criticised for their diverging nature. This work proposes using the feature of large language models (LLMs) to learn about the "data bias" injected by diverse SDG classifications into bibliometric data by exploring five SDGs. We build a LLM that is fine-tuned in parallel by the diverse SDG classifications inscribed into the databases' SDG classifications. Our results show high sensitivity in model architecture, classified publications, fine-tuning process, and natural language generation. The wide arbitrariness at different levels raises concerns about using LLM in research practice.